Intelligent systems for weather modification programs

ABSTRACT

Data including current locations of candidate clouds to be seeded is obtained; based on same, a vehicle is caused to move proximate at least one of the candidate clouds to be seeded. Weather and cloud system data are obtained from a sensor suite associated with the vehicle, while the vehicle and sensor suite are proximate the at least one of the candidate clouds to be seeded. Vehicle position parameters are obtained from the sensor suite associated with the vehicle. Based on the weather and cloud system data and the vehicle position parameters, it is determined, via a machine learning process, which of the candidate clouds should be seeded, and, within those of the candidate clouds which should be seeded, where to disperse an appropriate seeding material. The vehicle is controlled to carry out the seeding on the candidate clouds to be seeded, in accordance with the determining step.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 62/484,043, filed on 11 Apr. 2017, the complete disclosure of whichis expressly incorporated herein by reference in its entirety for allpurposes.

BACKGROUND

Aspects of the invention relate to weather modification; manned and/orunmanned aircraft and/or ground vehicles, including but not limited tounmanned aircraft vehicles (UAVs, also known as “unmanned aerialvehicles”); artificial intelligence; machine learning; and the like.

Weather modification refers to intentionally manipulating or alteringthe weather; the most common form of weather modification is cloudseeding to increase rain or snow. Cloud seeding involves dispersingsubstances into the air that serve as cloud condensation or ice nuclei.Cloud seeding can be done, for example, by ground generators,ground-based flare trees, plane, or rocket.

An unmanned aircraft vehicle or UAV, commonly known as a “drone,” is anaircraft without a human pilot aboard. UAVs are a component of anunmanned aircraft system (UAS); such systems typically include a UAV, aground-based controller, and a system of communications between the two.The flight of UAVs may operate with various degrees of autonomy: eitherunder remote control by a human operator or autonomously by onboardcomputers.

Artificial intelligence (AI) or machine intelligence (MI) isintelligence demonstrated by machines, in contrast to the naturalintelligence (NI) displayed by humans and other animals. Machinelearning is a field of computer science that gives computer systems theability to progressively improve performance on a specific task withdata, without being explicitly programmed.

SUMMARY

Aspects of the invention provide intelligent systems for weathermodification programs. In one aspect, an exemplary method includesobtaining data including current locations of candidate clouds to beseeded; based on the data including the current locations of thecandidate clouds to be seeded, causing a vehicle to move proximate atleast one of the candidate clouds to be seeded; obtaining, from a sensorsuite associated with the vehicle, while the vehicle and sensor suiteare proximate the at least one of the candidate clouds to be seeded,weather and cloud system data; and obtaining vehicle position parametersfrom the sensor suite associated with the vehicle. The method furtherincludes, based on the weather and cloud system data and the vehicleposition parameters, determining, via a machine learning process, whichof the candidate clouds should be seeded, and, within those of thecandidate clouds which should be seeded, where to disperse anappropriate seeding material. The method further includes controllingthe vehicle to carry out the seeding on the candidate clouds to beseeded, in accordance with the determining step.

In another aspect, another exemplary method includes obtaining, from aground-based sensor suite including a plurality of sensors, associatedwith a ground-based seeding suite including a plurality of seedingapparatus, weather and cloud system data; based on the weather and cloudsystem data, determining, via a machine learning process, whichindividual ones of the ground-based seeding apparatus to activate, andwhen; and sending control signals to the individual ones of theground-based seeding apparatus, to cause same to emit seeding material,in accordance with the determining step.

As used herein, “facilitating” an action includes performing the action,making the action easier, helping to carry the action out, or causingthe action to be performed. Thus, by way of example and not limitation,instructions executing on one processor might facilitate an actioncarried out by instructions executing on a remote processor, by sendingappropriate data or commands to cause or aid the action to be performed.For the avoidance of doubt, where an actor facilitates an action byother than performing the action, the action is nevertheless performedby some entity or combination of entities.

One or more embodiments of the invention or elements thereof can beimplemented in the form of a computer program product including acomputer readable storage medium with computer usable program code forperforming the method steps indicated. Furthermore, one or moreembodiments of the invention or elements thereof can be implemented inthe form of a system (or apparatus) including a memory, and at least oneprocessor that is coupled to the memory and operative to performexemplary method steps. Yet further, in another aspect, one or moreembodiments of the invention or elements thereof can be implemented inthe form of means for carrying out one or more of the method stepsdescribed herein; the means can include (i) hardware module(s), (ii)software module(s) stored in a computer readable storage medium (ormultiple such media) and implemented on a hardware processor, or (iii) acombination of (i) and (ii); any of (i)-(iii) implement the specifictechniques set forth herein.

Techniques of the present invention can provide substantial beneficialtechnical effects, as will be appreciated by the skilled artisan. One ormore embodiments base cloud seeding decisions on more relevant cloud andenvironmental data, as compared to prior art techniques, thereby moreaccurately placing seeding material, obtaining better cloud seedingresults, and the like. Refer also to FIG. 7 and accompanying text.

These and other features and advantages of the present invention willbecome apparent from the following detailed description of illustrativeembodiments thereof, which is to be read in connection with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a conceptual rendering of ‘Intelligent’ Systems for weathermodification (advertent or inadvertent) and cloud seeding programsand/or activities, according to an aspect of the invention.

FIG. 2 depicts system-wide, subsystem and component interfaces andconfigurations for autonomous UAS/UGV (UGV=unmanned ground vehicle)systems with adaptive control (i.e., airborne/ground-based ‘Intelligent’Systems), according to an aspect of the invention.

FIG. 3 depicts development and data flow processes of autonomous UAS/UGVsystems with adaptive control (i.e., airborne/ground-based ‘Intelligent’Systems). In this figure, ‘Product’ is the main goal of each step. TheQA, or quality assurance, and dissemination step is the delivery or theend point of each step (i-vii).

FIG. 4 depicts a computer system that may be useful in implementing oneor more aspects and/or elements of the invention.

FIGS. 5A and 5B (collectively, “FIG. 5”) present a table detailing anexemplary conceptual functional configuration of an ‘Intelligent’ Systemto identify, monitor and evaluate cloud seeding and/or weathermodification programs via airborne and ground approaches, according toan aspect of the invention;

FIG. 6 presents a table detailing exemplary lightweight and compactsensors for an ‘Intelligent’ System Sensor payload, according to anaspect of the invention;

FIG. 7 presents a table demonstrating non-limiting examples of howembodiments of the invention overcome some effectiveness limitingshortcomings of current cloud seeding activities; and

FIG. 8 is a block diagram showing exemplary data acquisition, dataprocessing, and control aspects, according to an aspect of theinvention.

DETAILED DESCRIPTION

Aspects of the invention provide techniques for conducting cloudseeding, advertent and/or inadvertent weather modification programsand/or activities. At least some embodiments provide an advancedengineering-science-based method adapted to enhance the safety of, plus,lower the footprint and cost of, contemporary weather modification(advertent and inadvertent), and/or cloud seeding operational andresearch programs or activities, while optimizing their effectiveness(compared to contemporary cloud seeding programs).

At least some embodiments advantageously use the information fromon-system sensors to guide seeding action, i.e., employ adaptivecontrol. Indeed, one or more embodiments focus on using ‘Intelligent’Systems, with adaptive control and functional capabilities as disclosedherein, for weather modification and cloud seeding programs andactivities configured as defined by a specified program requirement. Atleast some embodiments employ a ground-based ‘Intelligent’ System forseeding fog or airborne ‘Intelligent’ System for seeding low basestratiform clouds, elevated stratiform clouds and convective clouds.

It is worth noting that one or more embodiments further enhance currenttechniques and/or systems. For example, one current project involvingpotentially pertinent sensors and/or components suitable for use inconnection with unmanned systems mentioned herein includes, e.g.,Navy-funded Innovative Dynamics, Inc. SBIR/STTR-funded Phase II award,entitled “Atmospheric Icing Conditions Measurement System (AIMS).”Reference is made to the IceSight Ice Protection System Airborne IcingMeasurement System (AIMS) available from Innovative Dynamics Inc.,Ithaca, N.Y., USA. For UAVs, the Cloud Water Inertial Probe (CWIP)sensor of Rain Dynamics provides in-situ meteorological information formanned and unmanned aircraft. Reference is made to the Cloud waterinertial probe (CWIP) and the CWIP Fin available from Rain Dynamics LLCof Boulder, Colo., USA. Other useful devices are available from DropletMeasurement Technologies of Longmont, Colo., USA; for example, the CloudDroplet Probe is a useful instrument on large and mid-sized UAVs. Referto Sara Lance, Coincidence Errors in a Cloud Droplet Probe (CDP) and aCloud and Aerosol Spectrometer (CAS), and the Improved Performance of aModified CDP, JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY, volume 29,pages 1532-1541, October 2012 (hereinafter “Lance 2012”), herebyexpressly incorporated by reference herein in its entirety for allpurposes, although the skilled artisan will be generally familiar withsame. The Droplet Measurement Technologies back-scatter cloud probe withpolarization detection (BCPD) instrument has significant potential foricing detection with UAVs. Reference is made to K. Beswick et al., Thebackscatter cloud probe—a compact low-profile autonomous opticalspectrometer, Atmos. Meas. Tech. 7, 1443-1457, 2015 (hereinafter“Beswick et al. 2014”), hereby expressly incorporated by referenceherein in its entirety for all purposes, although the skilled artisanwill be generally familiar with same. Stratton Park Engineering Company(SPEC Inc.) of Boulder, Colo., USA is miniaturizing its cloud particleimager (CPI) and its other cloud spectrometers for use on UAVs. Refer toR. Paul Lawson et al., An overview of microphysical properties of Arcticclouds observed in May and July 1998 during FIRE ACE, J. Geophys. Res.106 (D14), 14989-15014, Jul. 27, 20101, (hereinafter “Lawson et al.2001”), hereby expressly incorporated by reference herein in itsentirety for all purposes, although the skilled artisan will begenerally familiar with same. Given the teachings herein, the skilledartisan will be able to employ elements and/or components mentionedherein, and the like, to obtain at least a portion of atmospheric and/orenvironmental information used by our one or more embodiments.

Some systems combine ground-based sensors with models and satelliteretrieved information. Meteorologists and analysts determine the bestlocations for seeding based on available nearby data and under whatconditions that material will yield precipitation that reaches thetarget area, and conceivably when to turn the generators on/off. Incurrent systems, the ground-based icing rate sensor, satellite data andthe satellite retrieved information are typically not representative ofthe part in the cloud that is relevant or relatable to where theinformation is needed to determine when seeding should start and/or stopor to evaluate seeding actions. One or more embodiments automaticallymeasure such information more accurately and from a more relevantlocation and use same to automatically initiate the seeding. At leastsome embodiments use this information to help ensure optimaleffectiveness and efficiency in near-real-time; automatically.

One or more embodiments provide a methodology to improve the performanceand evaluation of weather modification or cloud seeding programs oractivities using adaptive autonomous control of airborne and groundseeding systems, and prudently participate in solutions to mitigateinadvertent weather modification.

Weather modification or cloud seeding projects are typically implementedon cloud systems or portions of clouds that are naturally inefficient atconverting their moisture into precipitation on the ground targeted tofall in a defined area. Cloud seeding is intended to make clouds moreefficient precipitators independent of the cause behind theinefficiency. Cloud seeding has evolved to work effectively on cloudsystems that contained the ideal range of conditions conducive to theuse of cloud seeding technology. Contemporary cloud seeding technologiesmay be effectively applied to facilitate the water cycle efficiency.See, e.g.: DeFelice, T. P. (Ed.), American National Standards Institute,American Society Civil Engineers, & Environmental Water ResourcesInstitute Standard Practice guideline for the design and operation ofsupercooled fog dispersal programs (44-13), ASCE, Reston, Va., USA(hereinafter ANSI/ASCE/EWRI 2013); Langerud, D. (Ed.), ASCE StandardPractice for the Design and Operation of Hail Suppression Projects(39-15), ASCE, Reston, Va., USA (hereinafter ANSI/ASCE/EWRI 2015);DeFelice, T. P. (Ed.), ASCE Standard Practice for the Design andOperation of Precipitation Enhancement Projects (42-17), ASCE/EWRI,Reston, Va., USA (hereinafter ANSI/ASCE/EWRI 2017); DeFelice, T. P. etal., Extra area effects of cloud seeding—an updated assessment, Atmos.Res. 135-6, 193-203, 2014 (hereinafter DeFelice et al. 2014); Keyes, C.G. et al., Guidelines for cloud seeding to augment precipitation, 3rdedition, ASCE Manuals and Reports on Engineering Practice NO. 81, ASCE,Reston, Va., USA (220 pp.) (hereinafter Keyes et al 2016), the completedisclosures of all of which are expressly incorporated by referenceherein in their entireties for all purposes, although the skilledartisan will be generally familiar with same. Weather modificationprograms are conducted across the globe in regions where clouds haveconditions amenable to the use of weather modification technologies viaglaciogenic seeding agents, or hygroscopic or warm cloud seeding agents.

Operational cloud seeding projects have been conducted since the firsttests of cloud seeding agents, also called glaciogenic seeding agents(i.e., dry ice and silver iodide, AgI), in the middle 1940s. Cloudseeding for enhancing winter snowpack in western United States (US)mountainous areas is considered highly successful since the mid-1980s.Scientists assess the increase in precipitation amount at generally upto a 10% increase compared to nature using glaciogenic seedingmaterials, especially silver iodide (AgI) complexes. Clouds consideredtoo warm for glaciogenic seeding, or warm clouds, can also be seeded.This is commonly referred to as hygroscopic seeding. The results of warmcloud seeding or hygroscopic seeding are favorable but stillinconclusive. The results of seeding mixed phase convective clouds havebeen mixed and often inconclusive. The seeding of isolated individualclouds has led to definite, mostly positive changes in the precipitationamounts. Statistical and computer-based methods have evolved to minimizethe noise introduced by the complexity of these systems and by thestatistically small number of events for the objective evaluation ofoperational cloud seeding effectiveness. The environmental impact fromusing contemporary glaciogenic seeding agents is minimal, if any. Theenvironmental impact from using contemporary hygroscopic seeding agentsis also minimal at this time.

Current cloud seeding programs may provide about a 10% increase in theprecipitation amount (compared to normal) under certain glaciogenicseeding applications. The percentage increase has a large uncertaintyand also is not likely higher due primarily to the following factors;(a) complexity of the cloud systems and their interactions with theirsurrounding environment, (b) the readiness of the technologies to sensethe environment to be treated under weather modification activities isinadequate, (c) insufficient data, (d) measurements not made at anadequate spatial and temporal frequency to satisfactorily reproducetheir true natural state, and (e) the sensors themselves are designed tomeasure a dependent variable. For example, an instrument measures liquidwater content using a standard liquid water content probe. Liquid watercontent can be the same value for two clouds to be seeded, even thoughthe cloud drop sizes are different. The latter adds risk to thesuccessful result of the operation if the seeding strategy doesn'tadequately match with the true cloud microstructure or its dropletpopulation characteristics.

Manned aircraft are the most common platform for cloud seeding. Mannedaircraft do enable access to remote areas, despite; (i) their high costto operate and to maintain, and (ii) difficulties related to theiroperational risks, i.e., use in icing conditions and mountainousterrain. Furthermore in this regard, there is also pilot risk associatedwith use of manned aircraft for cloud seeding. One or more embodimentsadvantageously enhance pilot safety by reducing or eliminating the needfor manned flights and/or by enhancing the effectiveness of mannedflights and thereby reducing the number of manned flights required.Current cloud seeding or weather modification programs do not useheavily instrumented aircraft to conduct operations, unless there is aspecial research effort tied to the program. Even during a researchprogram this information is not used operationally, except as it appliesto the research. In the airborne case, one or more embodiments employmanned and unmanned aircraft, with instrumentation and adaptiveautonomy. In one or more embodiments, the system's sensor payload is apertinent part of this methodology and its successful implementation.The cost to secure the manned seed aircraft, its seeding system and aninstrumented aircraft to support research and development cost is high.They also have high maintenance costs and there are costs to certifyeach for flight.

In contrast, current ground seeding systems are many orders of magnitudeless costly to obtain and maintain. Ground seeding system deployment andoperation can occasionally be a challenge. For example, their sitingrequires modeling to ensure the seeding material gets into theappropriate clouds (e.g., Keyes et al 2016), especially in mountainous,hilly, and lightly vegetated, if any, arid terrain. In one or moreembodiments, the use of ‘Intelligent’ Systems on the ground providesadditional, often unavailable, environmental data and operationalseeding guidance similar to airborne ‘Intelligent’ System platforms, toin turn provide enhanced and even optimal system performance and seedingeffectiveness.

The shortcomings that plague current weather modification programs/cloudseeding programs can be minimized through research and developmentprograms directed toward optimizing current technologies used to manage“treatable” atmospheric processes. See, e.g., DeFelice, T. P., Ahigh-level atmospheric management program plan for the new millennium,J. Weather Modification, 34, 94-99 (2002)(hereinafter “DeFelice 2002”)and Golden, J. et al. Toward a new paradigm in weather modificationresearch and technology, J. Weather Modification, 38, 105-117(2006)(hereinafter “Golden and DeFelice 2006”), the complete disclosuresof both of which are expressly incorporated by reference herein in theirentireties for all purposes, although the skilled artisan will begenerally familiar with same. However, besides adding significant cost,most program sponsors obtain sufficient benefit from employing thecurrent technology. Hence, research and development funding remainsscarce at best.

One or more embodiments advantageously overcome the data gap required toidentify suitable clouds and to more smartly seed them such that theresult is positive in the indicated target area. One or more embodimentsadvantageously improve the following and/or improve other aspectsreducing the need to improve the following: the seeding materials, themethodology for conducting weather modification activities, thetechnologies (e.g., seeding system, models, decision support tools, dataprocessing system), for integrating new, ancillary and/or auxiliarytechnologies (i.e., improved and/or new more efficient technologies).The latter may require novel ways to apply the improved technologiesoperationally. Disclosed herein is guidance for implementing one or moreembodiments to conduct weather modification and cloud seedingprogram/activity operations and their evaluation. The guidance will alsohelp the skilled artisan to keep the cost of one or more embodimentsequivalent to or even less than that for current cloud seedingactivities.

Axisa, D. and DeFelice, T. P., Modern and prospective technologies forweather modification activities: a look at integrating unmanned aircraftsystems, Atmos. Res., 178-9, 114-124 (2016)(hereinafter, “Axisa andDeFelice 2016”) and DeFelice, T. P. and Axisa, D., Developing theframework for integrating autonomous unmanned aircraft systems intocloud seeding activities, J. Aeronautics & Aerospace Engineering, 5:172,001-006 (hereinafter, “DeFelice and Axisa 2016”) both provide backgroundinformation, and the complete disclosures of both are expresslyincorporated by reference herein in their entireties for all purposes,although the skilled artisan will be generally familiar with same. Oneor more embodiments expand capabilities of unmanned systems to address,e.g., cloud seeding operations involving seedable clouds with high cloudbases (more than 3,000 m above ground level), and/or intense updraftsand turbulence.

One or more embodiments employ airborne (i.e., fixed wing), ground-basedand other ‘Intelligent’ Systems for precipitation enhancement andaugmentation and hail suppression. Some embodiments use a ground-based‘Intelligent’ System for weather modification and cloud seeding programsinvolving seeding of fog, low-based stratiform clouds (i.e., bases atabout 3,000 m above ground level and lower) and orographic clouds. Someembodiments use an airborne ‘Intelligent’ System, and/or a ground-based‘Intelligent’ System, for low-based stratiform clouds, elevatedstratiform clouds, orographic clouds or convective clouds depending onprogram requirements. One or more embodiments also provide acomparatively more developed data management, adaptive autonomy,‘machine-learning’ as defined herein, and corresponding softwareframework, which enhances a foundation set by DeFelice and Axisa 2016,as compared to current cloud seeding program components, and theentities discussed elsewhere herein.

One or more embodiments provide a paradigm-shifting methodology andframework for using ‘Intelligent’ Systems during the performance (i.e.,identify, conduct, monitor) and evaluation of weather modification,cloud seeding and inadvertent weather modification programs/activities.One or more embodiments provide guidance for optimal success and regularintegration of newer technological capabilities designed to morecost-effectively achieve mission-driven objectives. One or moreembodiments are independent of the detailed design of a particular kindof ‘Intelligent’ System'. Although the primary airborne ‘Intelligent’System in one or more embodiments is fixed wing, disclosed herein arepertinent functional capabilities of the ‘Intelligent’ Systems needed toeffectively use one or more embodiments, including the adaptiveautonomy, from which the skilled artisan will be able to select avariety of suitable fixed or movable wing systems. In one or moreembodiments, the system should be able to safely support its maximumweight at takeoff during the most extreme atmospheric environment asdefined in the detailed description section. One or more embodimentsprovide a framework and methodology to enable effective and even optimaluse during weather modification programs and/or activities.

‘Intelligent Systems’ are autonomous systems with adaptive control orare adaptive autonomous systems. Refer, for example, to Dydek Z. T. etal., Adaptive Control of Quadrotor UAVs: A design trade study withflight evaluations, IEEE Transactions on Control Systems Technology,21(4), 1400-1406 (2013) (hereinafter “Dydek et al. 2013”), the completedisclosure of which is expressly incorporated by reference herein in itsentirety for all purposes, although the skilled artisan will begenerally familiar with same. These systems contain secure interfaceswith their on-board sensor payload, communication, navigation, datamanagement and software controlled components. They can typically alsointerface securely with other observing systems (for example, UAS/UGS)and/or other technologies to carry out required operational activitiesor to monitor and evaluate seeding operations. Autonomous systems couldcontain; autonomous unmanned aircraft or ground systems (UAS/UGS) withadaptive control, autonomous unmanned aircraft or ground vehicles(UAV/UGV) with adaptive control, unmanned aerial or ground systems(UAS/UGS) with or without adaptive control, unmanned aircraft or groundvehicles (UAV/UGV) with or without adaptive control, manned aircraft orground systems, rocket delivery of seeding material with or withoutautonomy and/or with or without adaptive control, instrumented towers(including with a seeding system), ground-based seeding systems with orwithout adaptive remote controls, instrumented balloons (including witha seeding system), mobile and static observing systems equipped withseeding systems, and also any combination of these systems, not simplyeach in isolation (e.g. UAV swarm, ground-based networked system).Adaptive control refers to the improved performance and increasedrobustness of an autonomous system by configuring its control system toadjust the UAS/UGV seeding action as a function of measurements (i.e.in-situ or remote sensing of an atmospheric/environmental parameter(s)).

In one or more embodiments, the autonomous systems with adaptivecontrol, or ‘Intelligent Systems,’ are guided by remote sensors (e.g.,ground-based, including radar/radiometer, profilers, aircraft ifavailable, and/or satellite), numerical weather prediction (NWP) models,and/or ‘in-situ or ‘Intelligent’ System platform-based sensor(s) systemsto provide target locations for the seeding, and contain a sensor suite(payload) that provides ‘in-situ’ atmospheric/environmental data neededto identify conditions suitable for seeding or other specifiedapplication. Each ‘Intelligent’ System seamlessly ingests, in nearreal-time, the sensor payload data (i.e., temperature, relativehumidity, wind, updraft velocity, aerosol size distribution and dropletsize distribution, and other as required), auxiliary/ancillary data(e.g., cloud locations, topography, seeding locations based onconvection or other defined criteria, information from other‘Intelligent’ Systems, satellites, radar, radiometer, data archives),NWP model data, seeding action data and autopilot or remote controldata. The seeding action, where and when to seed, are determined by theseeding system software that extracts ancillary/auxiliary (or ‘otherdata’), NWP model data and/or platform sensor data inputs. What seedingmaterial to dispense, if not pre-determined, is determined by platformsensor data, NWP model data, and, as needed, auxiliary/ancillary data.

In one or more embodiments, all data are quality controlled using asimple test and processed in real time. Each ‘Intelligent’ Systemnavigates toward candidate cloud areas (if mobile) or is activated tostandby (if static), based on the location coordinates obtained from theauxiliary/ancillary data inputs and processed onboard and/or with thehelp of computers at the ground control station. The navigation orautopilot, or remote control (if ground-based) system, includes remotecontrol or autopilot routine, software-in-the-loop (SIL) database,Mission Planner, radio telemetry and a central processing unit (CPU).The autopilot or remote control routine, SIL database and Missionplanner, or an autonomous controller or equivalent, nowcasts(“nowcasting” refers to the detailed description of the current weatheralong with forecasts obtained by extrapolation for a period of 0 to 6hours ahead) the real-time ingested ancillary/auxiliary locationcoordinates and platform sensor data. The output of the autonomyroutine, or equivalent, is then fed back into the navigation (autopilotor remote control) that allows the system to automatically adapt itspath accordingly with ongoing in-situ and remote sampling and NWP modelguidance as it heads to the new locations. In one or more embodiments,the latter is continuously updated throughout the flight, and the systemis capable for machine learning as described herein.

Once the ‘Intelligent’ System reaches an appropriate (preferably ideal)location, the adaptive navigation routine passes control and sensor datato the seeding system (i.e., seeding dispenser including seeding modeland corresponding software), and seeding begins. Seeding starts and endsonce the sensors indicate favorable and then unfavorable seedingconditions, respectively. The seeding cycle continues until the UAS mustreturn for fueling or there is an unsafe situation, at which time areplacement system will be in place to continue the activity. Each‘Intelligent’ System transmits all data to the ground control station(GCS) via telemetry for archive and computationally-intensiveprocessing. The results from computationally-intensive processing,including the ensemble-like near-real-time prediction of the optimaladapted path(s) and optimal seeding location(s), rate(s), duration(s)and material(s), based on the ‘in situ and remote sensor data, seedalgorithm, SIL database predictions, NWP model predictions, and thelike, are stored in the SIL database, validated, and sent back to the‘Intelligent’ System data management system during its operation.Additionally, in the specific case of ground-based ‘Intelligent’Systems, this includes which systems to turn on for seeding to ensuremaximum effect and efficiency. In one or more embodiments, each‘Intelligent’ System is able to communicate with others throughout aseeding program (a seeding program might last on the order of months oryears, for example). Furthermore, in one or more embodiments, one ormore intelligent systems are configured to communicate with otherintelligent systems, ground control systems, and/or emergency managementsystems, and the like.

The ‘Intelligent’ Systems, if not ground-based, can be usedindividually, or in tandem (i.e., 2 or more), in a networked swarm, orin a manner that achieves concurrent Eulerian and/or Lagrangian data,with or without profiling, to appropriately meet the requirements of theseeding activity. They may be used in conjunction with current cloudseeding technologies. For example, some embodiments are employed usingthe airborne (not ground-based) ‘Intelligent’ Systems in weathermodification and cloud seeding programs/activities designed forprecipitation enhancement or augmentation and hail suppression.Similarly, some embodiments are employed using ground-based‘Intelligent’ Systems in weather modification and cloud seedingprograms/activities designed for fog dispersal and precipitationaugmentation.

Similarly, ground-based (static, tethered, and mobile) ‘Intelligent’Systems can be used individually, or in a network configured to ensureoptimal coverage of the seeding material in the targeted cloud systemsto ensure that the cloud system's precipitation fell into the targetedarea. In one or more embodiments, the ground-based system has anautonomy component that is controlled remotely by NWP model guidance andits concurrent sensor payload. The latter identifies when cloud systemsare seedable, turns on all, one or none of the systems as a function ofthe environmental conditions as measured or simulated, and continues theseeding operation until the conditions have ended. The NWP modelguidance is based on the data from the intelligent ground systems (e.g.“nudging”) as well as mesoscale and regional NWP models. That set ofdata is processed and used in near-real-time to optimally control thestart- and stop-seeding actions as well as to control the type ofmaterial dispensed, and to keep track of the total amount dispensed. Thelatter is continuously updated in one or more embodiments, and thesystem is capable of machine learning as described herein. In one ormore embodiments the system also provides alerts for reloading theseeding materials, and communicating extreme weather conditions. Onceseeding ends, each system can typically continue to make measurements asrequired. Further, in at least some instances, non-seeding ‘Intelligent’Systems in an array of ground-based ‘Intelligent’ Systems can beemployed to collect data throughout the same period, concurrently withthe systems that were seeding.

One or more embodiments employ improved technologies, detail theconfiguration of their interfaces, and allow those technologies andrelevant software systems to evolve independently of their use. Refer tothe table of FIG. 5. The latter contributes to more streamlined cloudseeding operations that have smaller operational footprints and costs(compared to contemporary cloud seeding programs), while enhancing oreven optimizing their effectiveness. Furthermore, while at no additionalcost, data at temporal and spatial sensitivities to overcomepredictability or sparseness issues of environmental parameters thatidentify conditions suitable for seeding and how such might beimplemented are readily available beyond their operational use.Management and implementation concepts for utilizing the describedmethodology are also provided herein.

Thus, one or more embodiments provide a paradigm-shifting methodologyand framework for using ‘Intelligent’ Systems to identify, conduct,monitor and evaluate weather modification, cloud seeding and inadvertentweather modification activities. Refer to FIG. 1, which depicts anexemplary embodiment of the invention including a UAS 105-2, a UAS 105-1(collectively 105), and two UGVs 113 a, 113 b (collectively 113), itbeing understood that manned aerial and/or ground vehicles could be usedin other embodiments, and that fewer or more vehicles could be used;i.e., from one vehicle up to any desired number. The exemplaryembodiment also includes conventional ground-based seeding systems orstationary UGVs 121, 123, 125, which could be used by themselves or incombination with the aerial and/or ground vehicles. In a non-limitingexemplary embodiment, the UAS 105-1, 105-2 are launched from launch area103 and fly in tandem, and the UGVs 113 a, 113 b are stationary and/orare moved as needed; all work as a unit to ensure optimal targeting ofcandidate clouds 143 to in turn ensure that optimal results are achievedin the target area 115. In the non-limiting example of FIG. 1, each UAS105-1, 105-2 and/or each UGV 113 a, 113 b has a similar payload 127 andendurance. The two UAS-es, i.e., UAS1 (105-2; e.g., above cloudformation level/spotter) and UAS2 (105-1, e.g., near cloud formationlevel/seeder) fly toward one or more initial target clouds that areheading toward the target area. A non-limiting exemplary payload isdescribed in FIG. 6; each UAS/UGV can include a video camera as part of127.

It is worth noting that, in conventional seeding operations, cloudtargets are generally chosen visually by the meteorologist on the groundand/or the pilot in the aircraft. See, e.g., Keyes et al 2016 andANSI/ASCE/EWRI 2017. In the UAS the meteorologist on the ground may nothave a visual of the cloud target, and onboard video processing of cloudtargets can identify cloudy regions using stereo photogrammetricanalysis and automatic feature matching that reconstruct 3D cloudscenes. See Romatschke, U. et al. Photogrammetric Analysis of RotorClouds Observed during T-REX, 97th American Meteorological SocietyAnnual Meeting, Robert A. Houze, Jr. Symposium, #443, AMS, Boston.Mass., USA 2017 (hereinafter, “Romatschke et al. 2017”), the completedisclosure of which is expressly incorporated by reference herein in itsentirety for all purposes, although the skilled artisan will begenerally familiar with same. One or more embodiments make use ofinformation from the sensors 127 (including video camera, and see alsoFIG. 6); from Radar 101, 111; and/or from satellite 117 viacommunication links 133, 135, 137. Communications links 145, 147, and149 can be employed when ground-based systems 121, 123, 125 areutilized. Information from sensors on other UAS and/or UGVs can also beemployed via links 129, 137, for example, as well as via a suitable linkbetween UAVs 105-1 and 105-2 (omitted to avoid clutter). Suitablecommunication links can also be provided between the ground controlstation-GCS 109 and each UAS/UGV 105-2, 113 b, 105-1, 113 a and thesatellite 117; see, e.g., communication links 119, 139, and 141.Communications links (omitted to avoid clutter) can also be providedbetween the GCS 109, UAS 105-1 and UGV 113 a, for example. In anexemplary embodiment, the mesh network 131 between UAS1 and UAS2 iscapable of 100 Mbps data rates while line-of-sight operations and beyondline of sight are capable of 56 kbps and 2.4 kbps, respectively, forexample. This information is processed and/or stored on each UAS/UGV105-1, 105-2, 113 a, 113 b and/or on processing systems in GCS 109. TheUAS and UGV and any ground systems 121, 123, 125 are controlled byand/or from the ground control station 109. The isotherm levels denotedby 151 and 153 are provided for guidance related to operational concernsfor implementing an aspect of the invention for optimal effects (as willbe appreciated by the skilled artisan, levels between which silveriodide, as it comes into thermal equilibrium, most effectively activatesice phase for water; and threshold reflectivity for radar haildetection).

One or more embodiments include the regular integration of newertechnological capabilities designed to enhance their mission objectives.Although the primary airborne ‘Intelligent’ System is fixed wing in someembodiments, in general, embodiments can employ any ‘Intelligent’ Systemwith defined pertinent functional capabilities needed. One or moreembodiments are not limited to a particular kind of “Intelligent’System.' “Intelligent’ Systems' are autonomous systems with adaptivecontrol, or are adaptive autonomy systems; refer again to Dydek et al.2013. Refer also now to FIG. 2. Block 113 is generally representative ofUGVs 113 a, 113 b, while block 105 is generally representative of UAVs105-1, 105-2. Adaptive control refers to the improved performance andincreased robustness of an autonomous system by configuring its controlsystem to adjust the autonomous systems' seeding action as a function ofmeasurements. Autonomous systems are herein defined as, autonomousunmanned aircraft or ground systems with adaptive control, autonomousunmanned aircraft or ground systems without adaptive control, unmannedaircraft or ground systems (UAS/UGS) with or without adaptive control,unmanned aircraft or ground vehicles (UAV/UGV) with or without adaptivecontrol, manned aircraft or ground systems, instrumented towers(including those with a seeding system), ground-based seeding systemswith or without adaptive remote controls, instrumented balloons(including with a seeding system), mobile and static observing systemsequipped with seeding systems, rocket-delivered seeding material, andalso any combination of these systems, not just each in isolation (e.g.,UAV swarm, ground-based networked system).

Autonomous systems with adaptive control, or ‘Intelligent’ Systems, are,for example, guided by remote sensors (e.g., ground-based, includingradar/radiometer, profilers, aircraft if available, and/or satellite)and/or ‘in-situ or ‘Intelligent’ System platform-based sensor(s) and/orsensor system(s) to provide target locations for the seeding andtypically contain a sensor suite (payload) that provides ‘in-situ’atmospheric/environmental data as appropriate to identify conditionssuitable for seeding or other specified application.

In one or more embodiments, the ‘Intelligent’ Systems contain secureinterfaces with their on-board sensor payload, data management, models,‘machine-learning’ as defined herein, and software controlledcomponents. They can also interface securely with other observingsystems and/or other technology for use in weather modification programsto carry out operational activities or to monitor and evaluate seedingoperations (see, e.g., table of FIG. 5 for airborne and ground-basedsystems).

One or more embodiments are not limited to any particular design and/orany particular fabrication technique for the ‘Intelligent’ System itselfor for any component of the ‘Intelligent’ System, except for expressingits required capabilities as a function of its application, adaptiveautonomy, and the methodology and framework to be employed for their usein weather modification or cloud seeding program activities. That is,one or more embodiments provide a better way to perform weathermodification and cloud seeding programs given the ‘Intelligent’ Systemas generally disclosed herein.

One or more embodiments utilize a fixed wing ‘Intelligent’ System if arequirement is for precipitation enhancement and augmentation (althoughthis is not intended as a limitation unless recited in a particularclaim). One or more embodiments utilize ground-based ‘Intelligent’Systems (i.e., mobile or static autonomous UGV with adaptive control) ifthe requirement is for fog dispersal or involves seeding low-basedstratiform cloud systems (i.e., bases at about 3,000 m and lower)(although this is not intended as a limitation unless recited in aparticular claim). An airborne ‘Intelligent’ System could be used forlow-based stratiform clouds or a fog deck thicker than 700 feet (213meters). Similarly, ground-based ‘Intelligent’ systems could be usedwhen seeding elevated stratiform clouds and/or convective clouds.However, airborne ‘Intelligent’ Systems could preferentially be usedwhen seeding elevated stratiform clouds and/or convective clouds. Thefinal choice depends on the requirements provided by the programsponsor, as will be appreciated by the skilled artisan, given theteachings herein. One or more embodiments advantageously employ adaptiveautonomy, interfacial configuration of system components andcorresponding software framework, enhancing prior work of DeFelice andAxisa 2016. Non-limiting exemplary benefits, features and advantages ofone or more embodiments, as compared to the current weather modificationprogram and activities, will be appreciated by the skilled artisan giventhe teachings herein.

In one or more embodiments, a framework for developing ‘Intelligent’Systems and integrating them in weather modification activities asdefined encompasses three basic developmental components, namely:

-   -   1) Sensors integrated onto autonomous airborne or ground-based        platforms. The sensors measure meteorological state parameters,        wind in 3D, turbulence, and aerosol-cloud microphysical        properties in conditions that are conducive to seeding        stratiform cloud (including fog), and/or convective clouds, or        any combination thereof.    -   2) Algorithms that manage the collection, quality assurance        (QA), distribution, analysis and use remote sensing (e.g.        radar), in-situ real-time sensor data, and/or other data as        required to guide the platform towards suitable targets to        implement the seeding, in the case of airborne or mobile ground        systems. However, in the case of stationary ground Systems, the        equivalent algorithms control seeding start/stop, seeding rate,        and possibly seeding material use choice.    -   3) Deployment of each and all ‘Intelligent’ systems, including        in a configuration for which they will be used to carry out for        a specific mission.

In one or more embodiments, once each ‘Intelligent’ System successfullypasses through these steps it is ready for use to fulfill the missionrequirements. The ‘Intelligent’ System platform, which should be capableof supporting the weight of the payload, data management, and/or seedingsystems, should also be able to handle the turbulence in the atmosphericlevels it traverses. Refer to Axisa and DeFelice 2016, which also makesthe point that small UAS platforms, with or without autonomy and/oradaptive control, might be capable of carrying some seeding material inthe form of ejectable or burn-in-place flares. Weather modificationoperations will most likely require larger UAS, since they likely needto carry sensors, AgI acetone solution and/or salt micro-powder, unlessnewer technologies are developed and integrated onto these systems. Thelatter is easily accommodated by one or more embodiments.

Referring to the table of FIG. 5, one or more embodiments set functionalcapabilities for appropriate and even optimal ‘Intelligent’ Systems usedduring weather modification and/or cloud seeding activities, i.e.,sensor payload, on-board data processing system with remote access,corresponding software for all platform functions, control system, andcommunication components. In one or more embodiments, the control systemincludes an Intelligence, Surveillance, and Reconnaissance capabilitythat will, depending on application, include functionality shown in thetable of FIG. 5 and/or additional or alternative functionality. Thevideo information, along with its thermodynamic measurements,aerosol-cloud microphysical properties, satellite and/or other relevantsensor-retrieved cloud droplet effective radius can be used to identifysuitable seeding conditions in the identified clouds. It is noted thatthere is a subtle difference in the use of video between airborne,ground mobile and ground stationary Systems primarily as it relates towhen and how the video information is used. The ground platforms use thevideo to change the status of the System to standby and begin takingmeasurements; whereas the mobile system also uses video to help it staywithin the cloud. The airborne System uses video to determine if a givencloud is the one identified by the Mission Planner. These data, however,can also be used to ensure the validity of the control system, and/orfor additional or alternative uses.

In one or more embodiments, the ‘Intelligent’ Systems, especially ifairborne, are able to support the weight of the sensor payload, seedingsystem, data management, communications and software controlledcomponents/aspects in the most severe atmospheric conditions without anycomponent failure and operate to fulfill mission requirements forseeding. Given the teachings herein, the skilled artisan will be able toadapt, for example, a fixed-wing UAS to implement one or moreembodiments.

A schematic for an exemplary embodiment of airborne and ground‘Intelligent’ Systems communications and component interfaces and theirsubsystems is shown in FIG. 2. FIG. 2 shows an exemplary UAS controlroutine schematic for autonomous control of the vehicle(s) 105, 113.Data acquisition includes inputs from the onboard sensors 255, 275. Datais also obtained in one or more embodiments from the radar 101, 111covering the target area 115; see generally 293; as well as from thecameras 253, 273. Data processing steps are carried out by CPUs 257, 277to ensure that good quality data is passed to the algorithms 243, 263and models 241, 261, where high-level control is performed (i.e., ofseeding dispensers 245, 265 and of vehicles 113, 105 by remote control249 and autopilot 269, respectively). Data 293 can also be communicatedthrough ground stations 109-1 and 109-2 (collectively, 109) by telemetry289, 291, 251, 271, 283-1, 283-2. The mission planner 287-1, 287-2includes an algorithm that produces the coordinates where seedingconditions are predicted to occur, and passes those locations to the UASautopilot 269/UGV remote control 249, as the case may be. Once the UASis near these coordinates, the sensor algorithms 263, 243 andcoalescence model (executing on CPUs 277, 257 and/or computers 285-1,285-2) become active, and through a hierarchy of logic statements,determine the exact location to start seeding. One or more embodimentsemploy a simulator implementing software in the loop (SIL) technology281-2/281-1 to simulate the UAS flight characteristics, UAS payloadsensor data 275, 255, 263, 243, data 293 and mission planner output287-1, 287-2. These outputs are used to optimize the seed/no seedthresholds and targeting algorithm, saving the high cost of trial anderror approaches and ensuring success, and should provide a smallernumber of false positive seeding condition detections (compared tocurrent practices).

The sensor payload data 255, 275, seeding algorithm data 241, 261 andautopilot data 269 (or Remote Control 249 if ground-based) aretransmitted to a ground control station (GCS) 109 and thence to anoperations center 299 via telemetry 289, 291 and Ethernet 297, withpriority given to autopilot 269/Remote control 249 data when bandwidthis limited. With this setup, any mobile ‘Intelligent System, includingUAS1 105-1/UAS2 105-2 for airborne applications, only requirescoordinates uploaded via telemetry to navigate to the preferred locationfor initial targeting. All other navigation control can be done onboardthe vehicle (with pilot over-ride active at all times, for example). Theground ‘Intelligent’ System is capable of machine learning as describedherein. The GCS computer 285-1, 285-2 utilizes pre-defined flight plansfrom mission control software 287-1, 287-2 to generate initialnavigation coordinates for UAS1 105-1 and UAS2 105-2 and/or UGVs 113.Other embodiments could architect the location of navigation control inan alternative manner.

The airborne ‘Intelligent’ System is guided, in one or more embodiments,by using the ‘real-time’ in situ-based measurements and flight guidancefrom the GCS Mission planner 287-1, 287-2 and SIL database 281-1, 281-2to navigate the ‘Intelligent’ System autonomously to areas of suitabletemperature, relative humidity, updraft velocity, aerosol sizedistribution and droplet size distribution to implement optimal seeding.Optimal seeding means that seeding starts and proceeds at a rate thatwill yield maximum conversion of cloud water to precipitation that fallsin the intended location on the ground, or target area.Software-in-the-loop (SIL) technology, for example, is used in one ormore embodiments to integrate the data from past missions of a similarkind and to evaluate them to formulate a mission plan. The missionplanner 287-2, 287-1 ‘interrogates’ the SIL 281-1, 281-2 to find thebest matching mission that has been validated. The result of theinterrogation is a set of simulated flight characteristics, payloadsensor data, numerical weather prediction NWP model data (NWP1 and NWP2model data in data set 293) and seeding model output 261, 241 to carryout the mission using the data ingested up to that time. The simulatorroutine (within 287-2, 287-1; 281-2, 281-1; and 285-2, 285-1) is thenapplied to optimize the seed/no seed thresholds and targeting routines.Once the simulations are complete, field campaigns involving the System105 or 113 are conducted. The simulations are continuously updated. Thelatter embodies machine learning as described herein. This is preferredover the high cost of trial and error approaches and ensures success. Ithas an extended usefulness in conjunction with Ground Systems 113. Thesame software-in-the-loop (SIL) technology, for example, is used in oneor more embodiments to integrate the data from past missions of asimilar kind and to evaluate them to formulate a mission plan for futureoperations. The latter embodies to machine learning as described herein.

In the case of ground-based ‘Intelligent’ Systems, in one or moreembodiments, the control system (remote control and routine 249, SILdatabase 281-1, Mission Planner-287-1, telemetry or radio 283-1,computer 285-1) differs slightly from the airborne ‘Intelligent’ System(105) equivalent (respectively, 269, 281-2, 287-2, 283-2, 285-2). Insome embodiments, the control system is executed by NWP (see data set293) and/or seeding model guidance 241, 261, based on data orinformation from other techniques, the data from the mobile intelligentground systems 113, and stationary ground systems 121, 123, 125 andother data set parameters 293, and the data from the 105). Hence, in oneor more embodiments, a model turns the ground system on, not a human.There is still the ability for human over-ride in one or moreembodiments via the operations center 299. The deployed ground systems,upon deployment, are in standby with the ability to communicate with theoperations center 299 (see FIG. 2) and the video camera 253 isactivated. Once cloud is detected their full sensor system 255 isactivated, but they are not necessarily commanded to seed at that time.The ground-based ‘Intelligent’ System's video information 253,thermodynamic, aerosol-cloud microphysical properties, satellite and/orother relevant sensor-retrieved cloud hydrometeor parameter is processedby CPU 257 and as needed computer 285-1 to identify suitable seedingconditions in the identified cloud, to control the start and stopseeding actions, as well as to control the type of material dispensed(if specified in the program requirements), and to keep track of thetotal amount dispensed by dispenser 245 under control of CPU 257 and/orremote control 249 via telemetry 251, 291. If the ground-based System ismobile, its elevation is also added to the aforementioned. Unlike in theairborne ‘Intelligent’ System and in part the mobile ground-based‘Intelligent’ System, at least some embodiments of the static groundbased “Intelligent’ System' do not use the sensor payload information tomove the system to the ideal location; instead they use that informationto identify when the seeding system needs to be on standby, turnedon/off, to set the seeding rate, and, if not predetermined, to determinewhich seeding material needs to be dispensed. Further, in one or moreembodiments, non-seeding ‘Intelligent’ Systems in an array ofground-based ‘Intelligent’ Systems can collect data throughout the sameperiod, concurrently, with the systems that were seeding. Note thattelemetry is a non-limiting example of a communication technique; one ormore embodiments can generally employ wireless and/or wiredcommunications systems to transmit data and/or commands, it beingunderstood that wired systems would generally be employed forground-based assets that are stationary or have at most a limited rangeof motion.

Simultaneously with the aforementioned, and in at least some instances,primarily on ground control station computers 285-1, 285-2, eachground-based ‘Intelligent’ System's sensor data (e.g., air temperature,wind field, aerosol-cloud microphysical data, seeding data includingaltitude and terrain elevation, 255 and 293) combined with mesoscale andregional scale NWP models 293 are used to perform an ensemble ofsimulations and results assimilated in the SIL 281-1, 287-1, 285-1.These are analyzed for ensuring maximum seeding effectiveness, i.e.,which generators produce best results in specific meteorologicalsituations. When the sensor measurements from the ‘Intelligent’ System'and/or NWP model data match the assimilated ‘best results’ or thresholdcases assimilated in the SIL database (281-1), a signal is telemeteredback to 113 via 291 that arrives at the Remote Control 249 instructing249 to send the signal to 245 of each ‘Intelligent’ System 113 thattells the seeding system 245 to start seeding and stop seeding. This isrepeated for all ground ‘Intelligent’ Systems 113 involved in a project.The SIL database 281-1 builds a climatology over time making the systemmore intelligent. If the decision is to have a mobile ground-based‘Intelligent’ System seed, and wind direction is from the required winddirection range, the optimal end location of the mobile ground-based‘Intelligent’ System 113 is determined, passed back to the Remotecontrol 249 and this ground ‘Intelligent’ System 113 autonomously startsmoving and subsequently adapts its path accordingly as it moves towardthat ‘end’ point while seeding and making measurements. If at any timeduring the mobile System's path its altitude falls below the altitude atand below which seeding material will not make it to the target area(based on the ensemble of nowcasts), then seeding stops. The system 113is redirected to an appropriate higher location to maintain optimaltargeting of its seeding material, and seeding is restarted. As noted apriori the latter are continuously updated throughout the operations.The ‘Intelligent’ Seeding starts and ends once the sensors indicatefavorable and then unfavorable seeding conditions, respectively. Onceseeding from the mobile ground ‘Intelligent’ System 113 stops or thereis an unsafe situation, the System stops moving but continues makingmeasurements, at which time a replacement system is in place to continuethe activity accordingly. The ground control station (GCS) MissionPlanner 287-1 can also have pre-defined ‘drive’ routes plans frommission control software and the position data to generate initialnavigation coordinates for each ground based ‘Intelligent’ Systemrequired to be mobile. An ensemble of nowcasts can also be configured,run, and analyzed in near-real-time using the data obtained from one ormore embodiments (and not simply climatology), even while operations areongoing and not just after the fact, to determine where eachground-based non-mobile ‘Intelligent’ System should be located inspecific meteorological situations to yield optimal results in a targetarea. The corresponding activity in current weather modificationprograms is based on climatology (i.e., the spatially and temporallyaveraged meteorology for the same region) and usually separate from theoperational weather modification program/activities. In one or moreembodiments, all ‘Intelligent’ Systems can also communicate alerts forreloading the seeding materials, and about extreme weather conditions tothe operations center 299, other ‘Intelligent’ Systems 105, 113 andaircraft data set 293.

The ability to have the precipitation fall in an intended area on theground is known as targeting. Targeting is a complicated aspect of anyseeding operation/activity and is accommodated by the software, in oneor more embodiments. Systems of one or more embodiments are guided bysatellite or weather radar which sends data to the mission planner andSIL database components that then processes and passes updatedcoordinates in near real-time to the System's autopilot to navigate toregions of suitable convection for cloud seeding. One or moreembodiments have the ability to use in situ, near-real-time cloud systemrelevant data to support targeting, which is an enhancement overconventional cloud seeding capabilities via ground or airborneplatforms.

Referring to the table of FIG. 5, the mobile or stationary ‘Intelligent’System payload, including sensors designed to provide ‘real-time’ insitu-based measurements, passes temperature, relative humidity, andupdraft velocity, for example, through the sensor algorithms 243, 263 onits CPU 257, 277 to the Mission Planner 287-1, 287-2 and SIL databases281-1, 281-2. The SIL technology simulates the ‘Intelligent’ Systemflight characteristics, payload sensor and radar data and seeding modeloutput. This simulator is then applied to optimize the seed/no seedthresholds and targeting algorithm. Once the simulations are complete,seeding tests involving the System are conducted. The simulatorimplementing SIL simulates the System flight characteristics, withnavigation driven by sensor and radar data collected from the previoustests. The SIL simulator performance of the combined sensor and radartargeting algorithm can be evaluated, for example, by running anensemble of simulation scenarios. The simulations can be compared torelevant locations from actual flight paths flown on previous cloudseeding missions to understand differences in behavior between mannedoperations and those performed by the UAS. In one or more instances, thedata from the previous missions are previously uploaded to and stored inthe SIL database 281-1, 281-2. This comparative analysis serves asguidance for improving the algorithm and simulation software as it ispassed to the mission planner 287-1, 287-2. The mission planner usesthis data to update the flight paths and telemeter updated coordinatesback to the autopilot of the ‘Intelligent’ System, ensuring optimalautonomous navigation to areas of suitable temperature, relativehumidity, and updraft velocity. Existing datasets (e.g. see Axisa andDeFelice 2016) provide valuable sources of data to develop and constrainthe algorithms that guide the “‘Intelligent’ Systems.” These data can bemined, analyzed and features extracted to locate representativetime-series of key sensors from research aircraft flying at or belowcloud base (e.g., sensors that measure updraft velocity, aerosol sizedistribution and droplet size distribution). One example for determiningthresholds is the analysis of measured aerosol size distributions,hydrometeor size distributions and their relationship to the productionof rain. A broad drop size distribution with a tail of large drops mightnot be suitable for hygroscopic seeding, especially if large hygroscopicaerosol particles are present below cloud base. In general terms, andwhen seeding is warranted, droplets have a narrow drop size distributionand are smaller near the base of the cloud. These types of clouds can beregarded as having continental properties, with a relatively largenumber of small droplets that may inhibit the formation of rain. On theother hand, less numerous, larger droplets in the cloud favor thenatural formation of warm rain. The ‘in situ’ measured aerosol sizedistribution and hydrometeor size distribution data are similarly passedthrough the Sensor Algorithms 243, 263 to provide further guidance foroptimizing the seeding implementation and targeting of the seedingmaterial 241, 261, 249, 269, 245, 265. Hence, one or more embodimentsemploy and develop techniques to distinguish between these cloudproperties and apply the seeding material based on analysis and dataassimilation derived with in situ measurements and not observations thatare far removed and under-representative of the particular cloud systembeing seeded, as might be done in research weather modificationactivities or program evaluations.

Similar analysis can also be conducted on radar data in cloud regionsknown to be suitable for seeding, to establish representative radarsignatures for the corresponding periods and locations. The data can bequality assured and processed so that their output will be similar tothat produced by the System sensor payload. These data would then beanalyzed to develop and constrain the algorithms that guide the System,to finalize and test sensor payload algorithms; to perform the dataanalyses; and to develop the radar algorithm.

Operational weather modification programs typically use 5 cm weatherradar that often have Doppler capability for monitoring precipitationdevelopment and storm motion (e.g., with Doppler velocity field) duringoperations. Refer to Keyes et al 2016 and ANSI/ASCE/EWRI 2017. An‘Intelligent System’ 113, 105 equipped with radar software within itsmission planner 287-2 (and/or 287-1 in some embodiments) targetsconvective clouds (or cells) that may be viable seeding targets. Theseedable cloud targeting algorithm finds the most suitable cloud forseeding based on actual sub-cloud scale in situ microphysical data aswell as the conventional radar data. It also assimilates polarimetricDoppler weather radar data fields in dataset 293, which significantlyimprove current targeting guidance.

In one or more embodiments, the “‘Intelligent’ System” also contains aseeding system to carry out cloud seeding. The seeding system 245, 265is able to dispense hygroscopic flares and/or glaciogenic flares;solutions or powders of micro-particles or nanoparticles or otherdepending on the requirements. The seeding strategies using one or moreembodiments can, but need not necessarily, change as compared to thosecurrently used. For instance, cloud targets are identified differentlyin one or more embodiments versus conventional seeding operations.Conventionally, seeding targets (clouds) are chosen visually by themeteorologist on the ground and/or the pilot in the aircraft (e.g.,Keyes et al 2016; ANSI/ASCE/EWRI 2017; ANSI/ASCE/EWRI 2013; andANSI/ASCE/EWRI 2015). In one or more embodiments, the meteorologist onthe ground may not have a visual, and the pilot/operator of anairborne/ground unmanned system will often times be beyond the line ofsight. Observers beyond the line of sight may be present and unqualifiedto identify suitable clouds notwithstanding current regulation(furthermore in this regard, FAA rules do not currently allow UAS tooperate in cloud or beyond line of sight; they do allow centers ofexcellence to extend the line of sight beyond the origin point—therehave been exceptions). Hence, the visual in one or more embodiments ismade using onboard video processing of cloud targets. That informationcan be fed into the autonomy module along with other data inputs asnoted a priori, and the path of the “‘Intelligent’ System” modified innear real-time accordingly. The ‘Intelligent’ System pilot/operator willtypically have an option to override the autonomy from his/her controlpoint through the operations center 299. In contrast, a pilot of amanned aircraft carrying an adaptive technology, (i.e., for example,CWIP, see Axisa and DeFelice 2016), paired with a seeding system (e.g.,113 and/or 105), might be effectively used within one or moreembodiments, all other considerations of manned aircraftnotwithstanding.

Other examples include the use of icing rate sensor information withground seeding dispensers or generators, and ground sensors combinedwith models and satellite retrieved microphysical properties at cloudtop (e.g., Keyes et al. 2016, ANSI/ASCE/EWRI 2017). In such conventionalcases, especially in hard to reach orographic areas, the icing ratesensor information can be sent to the person responsible for turningon/off the seeding system. That person then calls the generator andpresses a button to turn it on or off; otherwise does so manually. Oneor more embodiments employ (as compared to prior art) more relevant andmore accurate data from a more relevant location to automaticallyinitiate the seeding. In another example, namely, ground sensors, thesame are combined with NWP models and satellite retrieved information,and manually used with best available nearbymeteorological/climatological data to determine the best locations forthe seeding generators and under what conditions that material willyield precipitation that reaches the target area, and conceivably whento turn the generators on/off. Ground-based icing sensor and satellitedata and satellite-retrieved information are typically notrepresentative of the part in the cloud that is relevant or relatable towhere the information is needed to determine when seeding shouldstart/stop or evaluate seeding actions. These are still manual effortsprimarily and are currently used post operations or as a piggyback to arare research activity. One or more embodiments, in contrast,automatically measure or obtain such information more accurately andfrom a more relevant location and use same to automatically initiate theseeding.

Pertinent aspects regarding how to equip the UAS platform with a seedingdispenser (or delivery) system will now be discussed. The skilledartisan will appreciate that the seeding dispenser system for‘Intelligent’ Systems used in one or more embodiments will be morerobust than simply mounting a AgI flare on a UAS platform and ignitingthat flare in flight, since using any UAS in an operational weathermodification program activity is many orders of magnitude moredifficult. Conventional technologies used operationally today on mannedseeder aircraft are not yet directly transferable to any UAS. Oneexample is the longevity of flight through supercooled cloud by the UASplatform. There are commercially available products that might be usedon a UAS platform. However, until such are tested for the particularsystem, which is an integral part of one or more embodiments, theautonomy would have to reposition the UAS to a warmer, dryer location.After losing the ice build-up, the vehicle then may be repositioned toseed the cloud top or at the cloudbase, for example, depending on thespecific meteorological situation. These issues are accommodated asstandard practice through one or more embodiments.

Cloud top seeding commonly uses droppable flares, which requireapproximately 600 to 1800 m, depending on burn time after ignition,before being completely consumed (Keyes et al. 2016). The ‘IntelligentSystem’ of one or more embodiments is able to accommodate such adistance and maintain minimum altitude restrictions as required byregulatory agencies, as well as ensuring that the seeding materialreaches the −5° C. vertical cloud level for AgI flares to become activeice nuclei. Conventional seeding aircraft can use AgI flares, amongother seeding materials. Successful cloud treatment for precipitationaugmentation typically requires in-cloud seeding rates of tens tohundreds of grams of AgI per kilometer, and hundreds to thousands ofgrams per hour when seeding the tops of large convective cloud systems(Keyes et al., 2016). In contrast, the use of AgI solutions fromground-based generators typically yields about 5 to 35 g of AgI per hourof operation (Keyes et al. 2016).

It is worth noting that the −5 C isotherm level inside a convectivecloud is used to determine the likelihood of ice (hail) depending on theRADAR reflectivity values for that general area. With the −5 C level andthe RADAR reflectivity value exceeding a threshold value (depends onwavelength of the radar) at that −5 C level, then there is likely to behail inside that system and the seeding strategy would have to change orstop depending on specific meteorological and cloud situation.

If an airborne UAS is required to use a seeding dispenser capable ofcarrying 100 flares, each containing 10 g of AgI (by weight), this wouldadd at least 4.3 kg of total extra weight from the flares alone (flareshave significant weight other than the payload; i.e., a flare has 43 gtotal weight of which 10 g is AgI). This does not take into account theadded weight of the entire seeding dispenser system (i.e., flare rack)though. The amount of AgI dispensed might yield a sufficient amount ofAgI to be successful at enhancing the precipitation efficiency of thatcloud system during its flight time. However, depending on the size ofthe program and its requirements, one might need multiple Systems toensure continuous seeding. An alternative would be to integrate a newtechnologically advanced seeding dispenser onto the System, which may ormay not require a new kind of seeding material or a modification toenable the use of currently used seeding materials. Assuming theoperational considerations of the system and the lightweight seedingmaterial delivery were accommodated, how the seeding material isdelivered and whether ‘Intelligent’ System platform would only carry itwould reach the appropriate part of the cloud is part of this aspect.This example used AgI, but similar concerns are applicable when therequirements call for seeding material to be a powder ofnanoparticles/microparticles, or a glaciogenic- or salt-containingsolution.

In one or more embodiments, the data management system for the‘Intelligent’ System used in this methodology has the computationalthroughput and capacity to handle the data volumes generated for atleast an entire program mission and activity. It encompasses theon-board data processing system (CPU and data storage) with remoteaccess, the configured interfaces with the control system (remotecontrol or autopilot, SIL database, Mission Planner, radio, CPU),seeding system (seeding model 241, 261 and seeding dispenser 245, 265,and corresponding software), communications (Telemetry is a non-limitingexample of suitable wireless communications) 283-1, 283-2, 291, 289,251, 271, sensor payload (including sensor algorithms) 255, 275, 243,263 and the auxiliary/ancillary (or other data including other‘Intelligent Systems’) connections (i.e. data set 293), as well ascorresponding models (in data set 293; as well as 287-1, 287-2, 241,261), ‘machine learning’ as defined herein, and software for allplatform functions. It seamlessly ingests, in near real-time, the sensorpayload data (e.g., temperature, relative humidity, 3D wind field,pressure, aerosol size distribution and droplet size distribution,liquid water content/ice water content' other as required),auxiliary/ancillary data (e.g., cloud locations, topography, seedinglocations based on convection or other defined criteria, informationfrom other ‘Intelligent’ Systems, satellites, radar, data archives),seeding action data and autopilot data (e.g. in data stores 247, 267 aswell as 283), (and optionally other appropriate data) in accordance withground control stations 109 and data set 293. It then performs a simplequality assurance (QA) on these data. Furthermore, if the communicationinterfaces between each sensor of the seeding payload, and eachcomponent of the seeding system, data processing functional component,software functional component including autonomous path planning, arenot optimized for a specific System and for a specific mission goal orfunction, then its measurements may be unusable scientifically and theiruse may misdirect the flight path, resulting in unfavorable results. Wehave found that the use of standard trade studies, as opposed to trialand error as is contemporary, is preferable to address the issuesrelated to seeding material, seeding delivery, targeting, sensorplacement and non-optimal interfaces with the platform/sensors. Anengineering trade study that investigates sensor performance, on anindividual basis and ultimately as a combined unit, as a function ofplatform integration and placement, is appropriate in some instances,for example.

In a non-limiting example, the data stores 247, 267 send their data toGCS computers and SIL database for use as described herein; i.e.,machine learning, but also backup or archive (retaining latest cloudcondition and location information, seeding material parameters, forexample). The data stores 247, 267 retain the information necessaryprimarily as a systematic failsafe such as in the case of loss of groundcommunication and/or sensor data for instance, and to streamlineoperational seeding. The data stores 247, 267 may thus have limitedstorage with primary storage on the ground (e.g. SIL database(s)). In anon-limiting example, upon the loss of sensor data, use data stored in247,267 and engage 269,249 to send a signal via 271, 251 to 293 and281-1, 281-2 or other source to retrieve relevant ‘other’ data tofulfill the missing information gap and continue with seeding missionprogram.

The seeding action, where and when to seed are determined by the seedingsystem software (i.e., seeding model 241, 261 and seeding dispenser 245,265, and corresponding software) that extracts ancillary/auxiliary (e.g.data set 293) and platform sensor data inputs 255, 275, with acapability to have pilot override 299. Determining when to seedprimarily uses the platform sensor data 255, 275) once ‘where’ to seedis determined by autopilot/remote control routines. What seedingmaterial to dispense, if not pre-determined, is, in one or moreembodiments, determined by platform sensor data, primarily, and asneeded auxiliary/ancillary or ‘other’ data. For example, using exampleselsewhere herein as a basis, the seeding model algorithm ingests andextracts the time-stamped sensor data entries, and then processes thesedata to extract those data that fall within threshold values ofoperational quantities to determine when to seed, and what material todispense if not predefined. The threshold values of operationalquantities come from the on-board sensor payload, which includesenvironmental (e.g., 3D winds, temperature, relative humidity,pressure), aerosol and cloud microphysical properties (e.g., aerosolconcentration, aerosol chemistry, aerosol hygroscopicity, dropconcentration, drop size distribution, effective drop size, hydrometeorsize, hydrometeor type, ice-cloud depolarization ratio). The data arequality controlled using a simple test (e.g., data range test), andprocessed in real time (e.g., passed through low pass filter) to providedata that describe the measured atmospheric/environmental parameter ofinterest (e.g., updraft velocity, droplet size and corresponding dropletconcentration, and aerosol size and corresponding concentrations). Thesedata are then passed through a series of if/then statements whichessentially encompass the threshold criteria to indicate seedability. Ifthe thresholds are met, then seeding occurs in accordance with theenvironmental conditions and the chosen material to be dispensed. Forexample, if hygroscopic seeding material is used, then it would likelybe dispensed at cloud base or at ground level; whereas glaciogenicmaterial might be dispensed at cloud base, cloud top or at ground level,while it and other systems continue to make sensor measurements, collectancillary/auxiliary data and manage their programmatic roles. Thethresholds are also used to establish natural variability, addressscientific research and analyses, and in some circumstances, arerelatable to control cases if seeding occurred in a nearby cloud.

In one or more embodiments, each airborne ‘Intelligent’ Systemautonomously navigates toward candidate cloud area where seeding isprobably effective, based on the location coordinates provided throughthe autopilot, which gained its coordinates via the Mission Planner287-1, 287-2. The location coordinates can be obtained, for example,from the auxiliary/ancillary data inputs and processed, such as by anavigation control-like module (autopilot/remote control 249, 269,mission planner 287-1, 287-2 and SIL database 281-1, 281-2), onboard thesystem (in one or more exemplary non-limiting embodiments, pilotover-ride is available via operations center 299 at all times (i.e., 24hours per day, 7 days per week during operational periods)). Thenavigation or autopilot, or remote control (if ground-based) system,includes remote control or autopilot routine 249, 269, SIL database281-1, 281-2, Mission Planner 287-1, 287-2, radio 283-1, 283-2(communicating with radios 251, 271 via telemetry 291, 289), CPU 285-1,285-2. Again, telemetry is a non-limiting example of communication. Theautopilot or remote control routine, SIL database and Mission planner,or an autonomous controller, or equivalent, nowcasts the real-timeingested ancillary/auxiliary location coordinates and platform sensordata. The output of the autonomy routine, or equivalent, is then fedback into the navigation (autopilot or remote control, 249, 269) thatallows this system to automatically adapt its path accordingly withongoing in situ sampling and NWP model guidance as it heads to the newlocations. The latter is continuously updated throughout the flight.Once the ‘Intelligent’ System reaches the ideal location, the adaptivenavigation routine passes control and sensor data to the seeding system(i.e., seeding dispenser 245, 265 including seeding model 241, 261 andcorresponding software), and seeding begins. Seeding starts and endsonce the sensors indicate favorable and then unfavorable seedingconditions, respectively. The seeding cycle continues until the UAS mustreturn for fueling or there is an unsafe situation, at which time areplacement system is in place to continue the activity, as appropriate.The ground control station (GCS) computer 285-2, 285-1 has pre-definedflight plans from mission control 299 software and the position data togenerate initial navigation coordinates for each ‘Intelligent’ System.

Similarly, based upon examples elsewhere herein for ground-based(stationary, mobile, and/or tethered) ‘Intelligent’ System, data fromits sensor payload or model simulated data to identify when systems areseedable, select the seeding material (if not predetermined), decidewhen to turn on all (if an array), one or none of the systems, andcontinue the seeding operation until suitable conditions have ended. Theseeding material selection is then based on meteorology andaerosol-cloud microphysics and meteorological data. The Seeding Systemsoftware tracks the use of the material and provides alerts forreloading the seeding materials. Each system also communicates extremeweather conditions. Once seeding ends, each system continues to makemeasurements as required. Further, non-seeding ‘Intelligent’ Systems inan array of ground-based ‘Intelligent’ Systems can be collecting datathroughout the same period, concurrently with the systems that wereseeding.

There are times when mobile (aircraft included) and static ‘Intelligent’systems will be appropriate. An example of such could be in anorographic region where snowpack augmentation or precipitation increaseis required. For sake of this illustration, seeding will happen using aglaciogenic material using an array of static ground ‘Intelligent’Systems and a single mobile ‘Intelligent’ System. The mobileground-based ‘Intelligent’ System is located at the furthest upwinddistance from the target area but in a wind direction (relative to thetarget area wind rose) that is not climatologically frequent. The latterwind sector usually yields treatable clouds but the other Systems in thearray, and a stationary ‘Intelligent’ system would not be effective inseeding these clouds until it was too late (meaning if they did have aneffect the precipitation would not likely fall in the target area).Furthermore, an airborne ‘Intelligent’ system is not feasible. Giventhese requirements, in the conventional cloud seeding scenario, theground seeding generators would either not be turned on, or they wouldall be turned on. Either situation would yield minimum benefit at atypical cost using the conventional scenarios currently used today.

In contrast, one or more embodiments are more cost effective and enhanceor even maximize the effectiveness of the seeding action, using in situdata from each ‘Intelligent’ System and other data to have one or allstationary ground ‘Intelligent’ Systems seed the cloud system. Further,when the wind direction was from the special wind sector, one or moreembodiments may, for example, primarily have the mobile ground generatorseed the cloud system. The latter requires that the mobile groundsystem, if it does start seeding, would stay with that cloud during theseeding until otherwise directed. As discussed elsewhere herein, theinformation from the past studies can be stored in the SIL database281-1, 281-2 along with the seeding thresholds. The database 281-1,281-2 can also contain detailed topography, road information, land covertype and morphology data for the target area and surrounding region.When these data determine that a ground ‘Intelligent’ System will notyield precipitation in its target area, the system will stop or not beseeding, and will be making measurements, for example. Likewise, themobile System seeds as it is moving when the wind direction is from thespecial wind sector, and the optimal end location of the mobileground-based ‘Intelligent’ System is not reached. It continuously adaptsits path and checks whether to keep seeding as it moves toward the endlocation while recording data. If at any time during the mobile System'spath the nowcasts indicate that it will be at an elevation at and belowwhich seeding material will not make it to the target area, then seedingstops; the system stops moving; but its sensors continue to makemeasurements. It shortly thereafter is autonomously directed to anappropriate location to maintain optimal targeting of the seedingmaterial until the seeding event stops. More details are providedelsewhere herein.

The “Intelligent” System that detects clouds amenable to seeding and thelocation of the seeding will, in one or more embodiments, involve thedevelopment of targeting, radar, and sensor algorithms in a mannersimilar to that highlighted elsewhere herein for developing seedthresholds from the sensors. This can be done, for example, by: (1)analyzing data from previous field campaigns to define key sensorparameters that input data into the cloud targeting algorithm, and (2)testing the performance of these algorithms through software-in-the-loop(SIL)-based simulator. Radar data are commonly used in conventionalcloud seeding programs and one or more embodiments can use radar data aswell to guide the navigation. In the case of ‘Intelligent’ Systems theradar data algorithm can contain features augmented by ‘in situ,’Intelligent' System sensor data and simple rule-based multi-thresholdseed/no seed algorithm. In order to make this algorithm robust, a cloudseeding model 261 (one non-limiting example is a coalescence box modelor the like) can be added that will run on the UAS data system CPU (notecorresponding UGV cloud seeding model 241). The box model ingests themeasured drop size distribution and calculates the time evolution of thedrop sizes and concentration into the near future. The result gives avery strong indication on whether the cloud is capable of producingdrizzle naturally (i.e. without seeding) and hence whether it should betargeted for seeding. This enhanced radar data algorithm, or equivalent,produces the coordinates where seeding conditions are predicted tooccur, and passes those locations to the Systems' autopilot. Once theSystem is near these coordinates, the sensor algorithms and coalescencemodel become active, and through a hierarchy of logic statements,determine the exact location to start seeding.

The enhanced radar data routine can be further enhanced by programing itto use polarimetric Doppler weather radar data. This is a significantadvancement beyond conventional Doppler weather radar guidance, if thesedata are available, because of the additional information provided bythe Polarimetric feature. This further enhancement uses a simple clouddroplet growth box model to calculate the evolution of the drop sizedistribution (DSD) starting with data ingested from a spectrometer thatmeasures the existing drop size distribution. The model performs athreshold comparison used to support the seeding decision result. Ifstarting with the basic radar data acquisition algorithm and it isdesired to use dual polarization data, improvement to handledual-polarization data will be appropriate. That provides the ability toidentify ZDR (Differential Reflectivity) columns and regions of highspecific differential phase between the dual polarization signals, bothindicators of significant precipitation. In addition, a hydrometeoridentification routine can be incorporated and improved, so thatdifferent microphysical regimes can be identified within a storm.

A quantitative precipitation estimation program should be available forthe estimation of precipitation rate at the ground, and can be used toverify the algorithm. The corresponding measured droplet sizedistribution (DSD) is ingested into the Seeding model which calculatesan ensemble of size distributions up to several minutes in the future.These distributions are compared against the metric seeding signature todetermine if seeding should begin, and/or stop. The distinguishingfeatures of a seeding effect is a DSD featuring an enhancedconcentration in the ˜15 μm to ˜22 μm diameter range due to the“competition effect” as described in Cooper, W. A. et al., “Calculationspertaining to hygroscopic seeding with flares,” J. Appl. Meteor., 36,1449-1469 (1997)(hereinafter, “Cooper et al. 1997), the completedisclosure of which is hereby expressly incorporated by reference hereinin its entirety for all purposes, although the skilled artisan will begenerally familiar with same. and a “tail effect” of enhancedconcentration of large droplets (˜22 μm to ˜30 μm diameter range) asdescribed in Rosenfeld, D. et al., “A quest for effective hygroscopiccloud seeding,” Journal of Applied Meteorology and Climatology 49(7) pp.1548-1562(2010) (hereinafter, “Rosenfeld et al. 2010”), the completedisclosure of which is also hereby expressly incorporated by referenceherein in its entirety for all purposes, although the skilled artisanwill be generally familiar with same. If the comparison matches theseeded metric DSD, an affirmative to begin seeding is passed by thealgorithm to the seeding routine.

Alternatively, if the number of drizzle drops that are produced by themodeled DSD exceed a certain threshold size and concentration a fewminutes into the simulation, that may indicate an active warm rainprocess and hence no seeding output is passed by the algorithm to theseeding routine. For example, if large drops >30 μm are produced in thebox model indicating active warm rain process is established; no seedingis recommended. One could estimate the rate of seeding required tomodify the measured DSD for a seeding effect and a tail effect. Thiswould benefit operations since it provides guidance for optimal seedingbased on actual in situ data and not arbitrary or derived multivariablevalues. A sensor payload that could provide the aforementioned data issummarized in the table of FIG. 6; the same could include, for example,an instrument that measures 3D wind velocity such as from themulti-angle inertial probe (MIP), which is simply the wind sensing partof the aforementioned Rain Dynamics CWIP, one that measures drop sizedistributions such as the aforementioned Droplet MeasurementTechnologies back-scatter cloud probe with polarization detection (theinstrument includes the ability to polarize the signal prior todetection and allows the software to determine phase of the hydrometeor,and for other shape related calculations) (see also Beswick et al.2014), and one that measures aerosol size distribution such as theHandix Scientific Portable Optical Particle Counter (POPS) availablefrom Handix Scientific LLC, Boulder, Colo., USA. Regarding the latter,see also Gao, R. S. et al., “A light-weight, high-sensitivity particlespectrometer for PM2.5 aerosol measurements,” Aerosol Science andTechnology, 50:1, 88-99 (2016) (hereinafter “Gao et al 2016”), herebyexpressly incorporated by reference herein in its entirety for allpurposes, although the skilled artisan will be generally familiar withsame. Given the teachings herein, the skilled artisan will be able toimplement one or more embodiments utilizing these sensors or the like.

Once each instrument is found to perform within specifications, it canbe integrated onto the airborne or ground system. Inter comparison dataobtained from a separate system will enhance the performance comparison.It will allow for testing of multiple instruments on different platformsand to constrain instrument errors.

The complexity of creating adaptive autonomy through a hierarchy ofalgorithms may produce limited functionality and reliability unless itis well designed, simulated and verified. The simulation includesrunning archived cases in the radar routine with a set of assimilatedaircraft observable parameters, then performing the simulation to see ifthe ‘Intelligent’ System finds the target cloud. Once the target cloudis reached in the simulation, the aircraft then switches to in-situsensing and finds the area of maximum threshold condition, startingseeding at the latter location, then finding the position of the minimumthreshold condition where seeding stops. The process repeats for varyingdynamic and microphysical conditions until the radar routine updateswith new target coordinates. The underlying hypothesis is that the radarroutine can be modified to not only nowcast the location of convectionwith real-time radar echo data input about the cloud environment, butalso with sensor data input from the System. The combination of radarand sensor data improves the ability to forecast optimal seedingconditions.

In a manner presented in detail elsewhere herein, the nowcast outputscan be simultaneously telemetered to and ingested into the simulator onGCS computers to simulate the performance of the combined sensor andradar targeting algorithm by running an ensemble of simulationscenarios. The simulations can be compared to relevant locations fromactual flight paths flown on previous cloud seeding missions tounderstand differences in behaviors between manned operations and thatperformed by the UAS. This analysis can serve as guidance for improvingthe algorithm and simulation software.

In one or more embodiments, each ‘Intelligent’ System transmits all data(i.e., sensors, seeding system, auxiliary/ancillary and autopilot orremote control) to the ground control station (GCS) via telemetry forarchive and computationally intensive processing. The results fromcomputationally intensive processing can be sent back to the‘Intelligent’ System data management system. Further, in one or moreembodiments, the reliable transmission of data among and between the‘Intelligent’ Systems and the operations center is ensured. One or moreembodiments include power back-ups in the event of power failure, aswell as standard graceful degradation schemes in the event of sensor ordata failures, incomplete data records, or bad data.

Once the ‘Intelligent’ System and its component development haveprogressed beyond passing their tests and their development cycles haveprovided results that have met the programmatic requirements, the‘Intelligent’ system is ready for deployment. In one or moreembodiments, readiness for deployment includes making sure not only thatthe sensors are providing acceptable data relative to primary standardsor no worse than field standards, but also that the autonomous UASsystem with adaptive control (Intelligent' System) algorithms areperforming within required specifications. The following scenario isemployed for illustrative purposes during an exemplary preparation fordeployment process, wherein one or more embodiments advantageously allowfor the complete range of component specifications and to ensure thatall possible, however remote, measured values by any and all sensors aretested. A suitable readiness procedure, in one or more embodiments, alsoconsiders the societal and regulatory issues surrounding the use ofinventive Systems. This advantageously helps to minimize delay.

Consider the following exemplary scenario wherein the weathermodification program requirement was to apply hygroscopic seedingmaterial. It involves programmed thresholds based on analysis ofexisting measured drop size distribution and their relationship to theproduction of rain, and similarly based on analysis of measured belowcloudbase aerosol size distribution data. A single non-ground-basedfixed wing ‘Intelligent’ System, equipped for seeding with hygroscopicmaterial (i.e., table of FIG. 5, airborne-seeding or middle capabilitiescolumn) and a sensor payload that includes the sensors in the table ofFIG. 6, flies through the candidate cloud and determines if the measuredvalues indicate a broad drop size distribution with a tail of largedrops. Then, after leaving the cloud, the vehicle automatically fliestoward and under its cloudbase level to find that same cloud's updraftwhile its processing system compares aerosol size distributions to theseeding threshold distribution. If large hygroscopic aerosol particlesare present below the cloud base, then seeding does not start, forexample.

We have found that this activity can be enhanced using, simultaneously,two fixed winged ‘Intelligent’ Systems in tandem, making this entireprocess occur quicker, with seeding starting sooner and optimizingsuccess with respect to increasing the efficiency of the cloud systemability to form rain and then having that additional rain fall in thedesignated watershed, for example. Using two ‘Intelligent’ Systemsinvolves one flying above cloud base concurrently with the other flyingjust below cloud base in the example. Each ‘Intelligent’ System has asimilar sensor payload, with, in this example, one at cloud base alsocontaining a seeding system. A conventional seed aircraft would notlikely carry the instrumentation to determine the below cloudbaseaerosol size distribution, and might have low resolution dropletdistribution information from somewhere above the cloud base estimatedby radar, radiometer or satellite sensors. The System (UAS1 105-1)climbs to the −5° C. isotherm 151 while the other system (UAS2 105-2)profiles downwind of UAS1 105-1. UAS1 (105-1) profiles the atmosphericparameters from the surface and up to the cloud top level and then fromnear surface up to cloud formation level (CFL) or the cloud base asspecific conditions warrant until the information about the existence ofa candidate cloud is received, at which time is moves to the operationslevel 151. UAS2 105-2 profiles the atmospheric parameters from thesurface and up to the higher operational atmospheric level 153 or thecloud top level as specific meteorological conditions warrant until theinformation about the existence of a candidate cloud is received. BothSystems fly in formation while approaching the candidate cloud 143 andwhile keeping a safe minimum separation of 100 to 300 m. Once near thecloud 143, each System assumes its position and commences its seedingmission profile where UAS1 105-2 penetrates the cloud and UAS2 105-1samples the cloud updraft near the CFL. Once the seeding mission stops,each System loiters, while collecting data, until each receives its nextaction directing it for more sampling inside the system it has justseeded, which may involve a series of cloud penetrations and sampling ofaerosols below cloud base (while maintaining separation).

One or more embodiments of the invention have an inherent framework thatautomatically facilitates the independent evolution of its technologieswithout disrupting their operational use. This translates intomaintaining streamlined cloud seeding operations, smaller operationalfootprints, and lower cost, while optimizing seeding operationseffectiveness (compared to current cloud seeding programs).

One or more embodiments of the invention provide smarter use ofavailable and preferably recently-developed technologies that yieldactual information about the actual environment in and around theactivity, and employ that information in near-real-time to adapt thepath toward the ideal seeding location, and use data from their sensorpayloads to optimize the seeding agent dispersal via their seedingsystems. In contrast, conventional cloud seeding methods, whether mannedaircraft or ground-based, rely on rudimentary meteorological dataavailable from manned aircraft, hour old environmental data, weatherradar data, model data, and archived data to carry out their seedingoperations, or operate from data averaged over a period longer than thecloud scale processes that does not coincide well with the time of theweather modification or cloud seeding activity was conducted, forexample. Hence, conventional operations have comparatively less data andpoorer quality data. This not only means less than optimal seedingeffectiveness, but also translates into less accurate evaluationresults. The latter is remedied by one or more embodiments and the dataused in conventional methods is still available, but becomesancillary/auxiliary.

Operational cloud seeding projects (ANSI/ASCE/EWRI 2013; ANSI/ASCE/EWRI2015; ANSI/ASCE/EWRI 2017; Keyes et al. 2016) are capable of dispersingsupercooled fog, increasing precipitation amounts by up to about 10% andpossibly minimizing the damage from hailstorms compared to naturalsystems, despite, for example; (a) complexity of the cloud systems andtheir interactions with their surrounding environment, (b) inadequatereadiness of the technologies to sense the environment to be treated (ina) under weather modification activities, (c) insufficient data (i.e.,remote areas are data starved and measurement systems are costly andlogistically involved), (d) measurements not made at an adequate spatialand temporal frequency to satisfactorily reproduce their true naturalstate, and (e) the sensors themselves are designed to measure adependent variable. For example, an instrument measures liquid watercontent. Liquid water content, which is commonly used in seedingoperations, can be the same value for two clouds to be seeded despitetheir cloud drop sizes being different. The latter adds risk to theresult of the operation, if the seeding strategy doesn't adequatelymatch with the true cloud droplet population characteristics.

The impact to the environment using contemporary glaciogenic seedingagents is minimal if any. It does not appear that there are any negativeenvironmental impacts due to contemporary hygroscopic seeding at thistime. The use of manned aircraft, which is arguably the most commonplatform for cloud seeding, does extend the application of contemporaryweather modification activities into remote and orographic regions. Seealso above discussion of pilot risk associated with use of mannedaircraft for cloud seeding. The cost to secure the manned seed aircraft,its seeding system and an instrumented aircraft to support research anddevelopment cost millions to obtain and have nearly equivalent costs tomaintain. In contrast, ground seeding systems are many orders ofmagnitude less costly to obtain and maintain in comparison. Groundseeding system deployment can be a challenge, usually in data-starvedregions, and their siting requires modeling to ensure the seedingmaterial gets into the appropriate clouds, especially in mountainous,hilly and lightly vegetated, if any, arid terrain (e.g., Keyes et al.,2016).

Referring to the table of FIG. 7, one or more embodiments overcome theshortcomings of the current methods for cloud seeding activities. One ormore embodiments also comparatively improve seeding effectiveness andthe significance of their evaluation. One or more embodiments, as aresult, may even change the strategies employed in seeding compared tothose currently used. One or more embodiments provide the value addeddata to support research and development efforts required to ensure suchshortcomings remain insignificant, until an organized nationalcomprehensive research and development program can be established (e.g.,DeFelice, 2002; Golden and DeFelice, 2006). Current operational weathermodification/cloud seeding activities rarely include research anddevelopment tasks due to their high cost and low additional benefit tothe operations program sponsor. Current program sponsors obtainsignificant benefit from employing the current technology. Hence,research and development funding remains scarce at best.

One or more embodiments of the invention do not require the need for anymanned aircraft with or without autonomous-adaptive sensors, hence thatrisk is mitigated. Those pilots can be cross-trained to fly and monitorthe “‘Intelligent’ Systems,” and the maintenance team trained to handleany maintenance. The costs of the systems, and their maintenance in thismethod is a factor of 5 to 50 times less than their manned, largercounterparts of the current, contemporary method, even after accountingfor the newness of any technology, longevity of platform and retraining.

Relative to using one or more embodiments, it is cautioned that, eventhough small UAS have operated successfully in the vicinity ofthunderstorms, and technologically can be used to conduct weathermodification research and operations, thorough safety investigationshould be made in a particular use case; for example, the several issuesand risks should be analyzed via engineering trade studies for weathermodification activities before adopting them as set forth with regard toFIG. 3 and detailed description elsewhere herein. The cloud systemsassociated with weather modification activities are often complex,and/or they occur in regions with complex terrain or ecosystems. Hence,appropriate safety considerations should be observed; for example, thepilot/driver of a UAS, and especially tied to an “Intelligent’ System'used for weather modification activities, should at least have anequivalent amount of flight time and training as would a pilot formanned aircraft weather modification activities. In a non-limitingexample, assume that the ‘Intelligent’ System is appropriately sized,optimally configured with respect to each functional mode (see table ofFIG. 5), and configured (see FIG. 2) appropriately for each weathermodification program activity.

The implementation of “Intelligent’ Systems', the adaptability of theiruse to multiple applications and ability to continually infuse newtechnologies is generally achievable by the skilled artisan, given theteachings herein, coupled with the adaption of industry-disciplinedscience and engineering fundamentals and their application for cloudseeding, weather modification operations and research and development,particularly optimized for using “Intelligent’ Systems' for advertentand inadvertent weather modification and/or cloud seedingprogram/activities. The following are provided to facilitate theunderstanding of embodiments of the invention and to help ensure thesuccessful use of embodiments of the invention by the skilled artisan.Given the latter, the process for developing the ‘Intelligent’ Systemfor either role (seeding, evaluation/monitor, or both) can generallyfollow these steps:

-   -   i. Determine and verify the requirements of the application.    -   ii. Identify, design, develop, test and document the sensing        payload that will optimally provide temporal, spatial (and        spectral) sensitivities within the requirements set under        step (i) to overcome the predictability or sparseness of        environmental parameters, the threshold values for seeding.    -   iii. Design, develop, test and document the information        processing system for producing and disseminating the        information obtained by the sensing suite from step (ii). This        includes using the in situ and other real-time sensor data to        guide the platform, if an airborne ‘Intelligent’ System, towards        suitable targets to implement the seeding, and the use of the        payload data to identify suitable conditions for optimal        seeding; or alternatively if a ground-based ‘Intelligent’        System, using the in situ and other real-time sensor data to        identify suitable conditions for optimal seeding,        initiate/terminate seeding and dispense the appropriate        material.    -   iv. Design, develop, test and document the Command, Control,        Communications, Computers, Intelligence, Surveillance, and        Reconnaissance (C4ISR) for this system. This includes        verification of the proper operation of the simulation (and        machine learning) software modules, including training with any        available training corpus and verification with any available        test corpus.    -   v. Design, develop, test and document the optimal integration        scheme of the payload sensor suite, processing system,        protection, and C4ISR (defined in table of FIG. 5) components on        the sensor suite identified under step (i).    -   vi. Integrate (ii) through (iv) and test operability.    -   vii. Perform optimization trade studies as needed. Return to        step (ii) if the result changes design.    -   viii. Field test, develop, deploy, and maintain system.

FIG. 3 illustrates the processing of the Sensor 275, 255 and other data293 into quality data used to determine when to start/stop seeding, orwhere the candidate cloud is located, for example. This data processingflow occurs with an optimal interaction between an engineering processand a development or scientific process. The data processing system is apertinent aspect of the “‘Intelligent’ System”. As will be appreciatedby the skilled artisan, the effort under each of the steps (i)-(viii)can be similarly demonstrated as steps 301, 303, 305, 307, 309, 311, 313under FIG. 3. Each step (i-viii) is implemented in an adaptabledevelopment cycle. That is to say, in one or more exemplary situations,the steps 301-313 of FIG. 3 can be repeated for each of the Romannumerals (i)-(viii). Furthermore, step (vii) is intended to not onlyimprove the performance as an entire system but it also provides theopportunity to insert new technologies. Axisa and DeFelice 2016, andDeFelice and Axisa 2016 provide some additional details. Thus, as noted,FIG. 3 depicts development and data flow processes of autonomous UAS/UGVsystems with adaptive control (i.e., airborne/ground-based ‘Intelligent’Systems). In this figure, ‘Product’ is the main goal of each step. Forexample, the main goal of step (i) is the requirements agreed upon forthe seeding program contracted. The main goal of Step (ii) is toidentify the sensors to be used in the operational program, such asthose in table of FIG. 6. The main goal of the sum total of steps(i-viii) is an operational version of vehicles 113 and/or 105. The QA,or quality assurance, and dissemination step 313 includes the deliveryor the end point of each step i-vii to be tested in the field or in anactual operation. Note that “QF1”=Quality Flag per first pass. The arrowbetween “science process” 305 and step 307 is a two-way arrow.

FIG. 3 thus depicts an exemplary fundamental process including anengineering process and a science Development process 305, and how theyinteract. In one or more embodiments, each step (i)-(viii) isimplemented following this same process as illustrated in FIG. 3 asappropriate according to the details. Step (iii) provides acomprehensive example of what is illustrated by FIG. 3. Following FIG.3, in the example, begin with the data collected (corresponding to‘Sensor’ block 301); make sure the information to be used in theengineering trade study is quality assured (ingestion and QA in block303); then perform the trade study and corresponding experiments thatyield results; analyze those results (block 307); and produce the finalproduct or output as needed per situation in block 309. In one or moreembodiments, the results are disseminated to the next and/or allsubsequent steps (i-viii), via documentation, a suitable data structure,or the like. For each pass through the entire set of steps (i-viii),reset the quality flag (QF) and start all over; it being noted thatfollowing Product block 309 QC and dissemination to end user 313 takeplace in block 311.

FIG. 3 thus illustrates, for example, the Sensor 275, 255 and other data293 being processed into quality data used to determine when tostart/stop seeding, or where the candidate cloud is located, forexample. This data processing flow occurs with an optimal interactionbetween an engineering process and a development or scientific process.

One or more embodiments of the invention can advantageously employinnovative seeding materials based on potential seeding agenttechnologies and delivery technologies after successful completion of anintegrative development process as disclosed herein. This ensuresoptimal and long lasting successful use of each ‘Intelligent’ System inweather modification or cloud seeding activity.

Regarding seeding agent technologies, see, for example, Carrasco J, etal., “A one-dimensional ice structure built from pentagons,” NatureMaterials, 8(5), 427-431 (2009) (hereinafter, “Carrasco et al. 2009”);Lou, Y. G. et al., “A comparative study on preparation of TiO2 Pelletsas photocatalysts based on different precursors,” Materials ScienceForum, 475-479, 4165-4170 (2005) (hereinafter “Lou et al. 2005”); Zhang,W. et al., “Photocatalytic TiO 2/adsorbent nanocomposites prepared viawet chemical impregnation for wastewater treatment: a review,”AppliedCatalysis A: General, 371(1-2), 1-9. (2009) (hereinafter “Zhang et al.,2009”). The complete disclosures of Carrasco et al. 2009, Lou et al.2005, and Zhang et al., 2009 are hereby expressly incorporated herein byreference in their entireties for all purposes, although the skilledartisan will be generally familiar with same.

Regarding delivery technologies, see, for example, Hill, G. E.,“Laboratory calibration of a vibrating wire device for measuringconcentrations of supercooled liquid water,” J. Atmos. Ocean. Technol. 6(6), 961-970 (1989) (hereinafter “Hill 1989”); Hill, G. E., “Radiosondesupercooled liquid water detector,” Final Report delivered in Septemberto U.S. Cold regions Research & Engineering Lab., Hanover, N.H. forContract DACA 89-84-C-0005 (1990), Atek Data Corp, 2300 Canyon Blvd.,Boulder, Colo. 80302 (97 pp.) (hereinafter “Hill 1990”); and Hill, G. E.et al., “A balloon-borne instrument for the measurement of verticalprofiles of supercooled liquid water concentration,” J. Appl. Meteorol.19, 1285-1292 (1980) (Hereinafter “Hill and Woffinden 1980”). Thecomplete disclosures of Hill 1989, Hill 1990, and Hill and Woffinden1980 are hereby expressly incorporated herein by reference in theirentireties for all purposes, although the skilled artisan will begenerally familiar with same.

Successful implementation of one or more embodiments can be optimized byemploying the following management and implementation guidance, which isan extension beyond the current standard industry practices in weathermodification programs. Refer to ANSI/ASCE/EWRI 2013; ANSI/ASCE/EWRI2015; ANSI/ASCE/EWRI 2017; and Keyes et al. 2016.

In particular: (i) Ensure and adapt a viable, program(/activity)-specific tailorable balance between practice, societal,science, technology and engineering cultures during the entire programthrough a program management entity (herein arbitrarily labeled PMO).(ii) The PMO is led by a single person (Program Lead) tied to only oneentity, organization or company, and contains solution-definedfunctional core members. The PMO lead implements the Program, ensuringhorizontally and vertically integrated communication among the entireProgram workforce with appropriate communication frequency and contentwith program sponsor and stakeholders. (iii) Each functional team is ledby its leader (who is a core functional member of the PMO). (iv) Eachteam should be allowed to work ‘pseudo’-autonomously on a self-containedcomponent during the project. No team deliverable, especially, should be‘owned’ by multiple teams. (v) Ensure an innovative communicative, safeenvironment that respects each entity's policies and propriety concerns,while enabling all to improve activities without being bound bytraditional ‘boxes.’ The latter would not penalize one for having adifferent idea. (vi) All staff (assuming they have the relevant skills),stakeholders and/or sponsors, and generally everyone want theactivity/program to succeed, want or are willing to get along with allothers, and want or are willing to collectively bring their best effortsforward always. (vii) Have plans to overcome challenges from; culturaldifferences, the ways ‘we used to do these programs’, and thinking outof their normal ‘boxes’.

Use of one or more embodiments advantageously overcomes the technicaland operational challenges of current, cloud seeding or weathermodification activities. This is especially significant when factoringin the increasing population growth and increasing desertificationacross the globe (whether rooted in inadvertent weather modificationactivity or not). There is an impending life critical need to overcomethe data gap required to identify suitable clouds and to smartly seedthem such that the result lands in the indicated target area. Thence theneed to improve on the seeding materials, the methodology for conductingweather modification activities, the technologies (e.g., seeding system,models, decision support tools, data processing system), for integratingnew, ancillary and/or auxiliary technologies (i.e., improved and/or newmore efficient technologies).

The following references are also incorporated herein by reference intheir entireties for all purposes, although the skilled artisan will begenerally familiar with same: Bates, T. S. et al., “Measurements ofatmospheric aerosol vertical distributions above Svalbard, Norway, usingunmanned aerial systems (UAS),” Atmos. Meas. Tech. 6, 2115-2120 (2013)(hereinafter, “Bates et al. 2013”); Elston, J. S. et al., “The tempestunmanned aircraft system for in situ observations of tornadicsupercells: design and VORTEX2 flight results,” Journal of FieldRobotics, 28(4), pp.461-483 (2011) (hereinafter, “Elston et al. 2011”);Lin, Po-Hsiung, “Observations: the first successful typhooneyewall-penetration Reconnaissance flight mission conducted by theunmanned aerial vehicle,” Aerosonde. Bull. Am. Meteorol. Soc. 87,1481-1483 (2006) (hereinafter, “Lin 2006”); and Ramana, M. V. et al.,“Albedo, atmospheric solar absorption and heating rate measurements withstacked UAVs,” Q. J. R. Meteorol. Soc. 133, 1913-1931 (2007)(hereinafter, “Ramana 2007”).

Embodiments thus provide a paradigm shifting methodology and frameworkfor using ‘Intelligent’ Systems (as defined elsewhere herein) during theperformance (i.e., identify, conduct, monitor) and/or evaluation ofweather modification and cloud seeding activities for precipitationenhancement and augmentation, hail suppression and fog dispersal.Embodiments provide, for example, one, some, or all of a smalleroperational footprint, safer operations, use of in situ and remotesensor data to guide and evaluate program activities, more versatile andautonomous seeding platform, and enhanced ‘practice’ framework.

One or more embodiments are also applicable for quantifying the extentof, and for supporting the objectives of a multi-disciplinary solutionfor inadvertent weather modification activities.

‘Intelligent’ Systems in one or more embodiments include autonomoussystems with adaptive control. Autonomous systems could be autonomousunmanned aircraft or ground (mobile, tethered and stationary) systemswith adaptive control, autonomous unmanned aircraft or ground systemswithout adaptive control, unmanned aircraft or ground systems (UAS/UGS)with or without adaptive control, unmanned aircraft or ground vehicles(UAV/UGV) with or without adaptive control, manned aircraft or groundsystems, Rocket delivery of seeding material with or without autonomyand/or with or without adaptive control, instrumented towers (includingwith a seeding system), ground-based seeding systems with or withoutadaptive remote controls, instrumented balloons (including with aseeding system), mobile and static observing systems equipped withseeding dispensers, and also any combination of these systems, not justeach in isolation (e.g. UAV swarm, ground-based networked system). Asused herein, ‘adaptive control’ refers to the improved performance andincreased robustness of an autonomous system by configuring its controlsystem to adjust the seeding action as a function of measurements as itfulfills its mission objective, (e.g., target and implement seeding,evaluate seeding effectiveness) also referred to as adaptive autonomy.‘Adaptive control’ for ground-based autonomous systems refers to theimproved performance and increased robustness to identify when theseeding system needs to be turned on/off, determine seeding rate, andultimately which seeding material needs to be dispensed.

Exemplary autonomous systems with adaptive control, or “‘Intelligent’Systems,” can be guided by remote sensors (e.g., ground-based, includingradar/radiometer, aircraft if available, and/or satellite) and/or ‘insitu’ or ‘Intelligent’ System platform-based sensor(s) to provide targetlocations for seeding. The sensor suite (payload) provides ‘in situ’atmospheric/environmental data needed to identify conditions suitablefor seeding or other specified action.

‘Intelligent’ Systems of one or more embodiments, especially ifairborne, are typically capable of carrying the weight of the sensorpayload, seeding system, data management and software controlledcomponents/aspects (refer to the table of FIG. 5) in the most severeatmospheric conditions without any component failure and operate tofulfill mission requirements for seeding.

‘Intelligent’ Systems of one or more embodiments contain secureinterfaces with their on-board sensor payload, data management, modelsand software controlled components. ‘Intelligent’ Systems can alsointerface securely with other observing systems and/or other technologyfor use in weather modification programs to carry out operationalactivities or to monitor and evaluate them.

The data management system for the ‘Intelligent’ System used in one ormore embodiments has the computational throughput and capacity to handlethe data volumes generated for at least an entire program mission andactivity. The data management system encompasses the on-board dataprocessing system with remote access, the configured interfaces with thecontrol system, seeding system, communications, sensor payload and theauxiliary/ancillary connections, machine learning as described herein,as well as corresponding software for all platform functions. In one ormore embodiments, the data management system seamlessly ingests, in nearreal-time, the sensor payload data 255, 275 (i.e., temperature, relativehumidity, 3D wind field, pressure, aerosol size distribution and dropletsize distribution, other as required), auxiliary/ancillary data 293(e.g., external source information about cloud locations, topography,“standard” flight paths (i.e. work with local regulatory authority toobtain pre-approval for certain flight paths), seeding locations basedon convection or other defined criteria, information from other‘Intelligent’ Systems 113, 105, satellites, radar, data archives),seeding action data 245, 265 and autopilot 269, 249 data. The systemwith the help of a ground control station computer performs a QA onthese data.

Exemplary improvement obtained in one or more embodiments includes aseeding action, where and when to seed determined by the seeding systemsoftware that extracts ancillary/auxiliary and platform sensor datainputs, with a capability to have pilot override. When to seed primarilyuses the platform sensor data once ‘where’ is determined. What seedingmaterial to dispense, if not pre-determined, can be determined byplatform sensor data, primarily, and as needed auxiliary/ancillary data.For example, the seeding system software ingests and extracts thetime-stamped data entries that fall within threshold values ofoperational quantities to determine when to seed, and what material todispense if not predefined. The threshold values of operationalquantities come from the on-board sensor payload, which includeenvironmental (e.g., 3D winds, temperature, relative humidity,pressure), aerosol and cloud microphysical properties (e.g., aerosolconcentration, aerosol chemistry, aerosol hygroscopicity, dropconcentration, drop size distribution, effective drop size, hydrometeorsize, hydrometeor type, ice-cloud depolarization ratio). The data arequality controlled using a simple test (e.g., data range test), andprocessed in real time (e.g., passed through low pass filter) to providedata that describe the measured atmospheric/environmental parameter ofinterest (e.g. updraft velocity, droplet size and corresponding dropletconcentration, and aerosol size and corresponding concentrations). Thesedata are then passed through a series of if/then statements whichessentially encompass the threshold criteria to indicate seedability. Ifthe thresholds are met, then seeding occurs in accordance with theenvironmental conditions and the chosen material to be dispensed. Forexample if hygroscopic seeding material is used, then it can, forexample, be dispensed at cloudbase or at ground level; whereasglaciogenic material might be dispensed at cloud base, cloud top or atground level, while it and other systems continue to make sensormeasurements, collect ancillary/auxiliary data and manage theirprogrammatic roles. The thresholds are also used to establish naturalvariability, address scientific research and analyses, and in somecircumstances, are relatable to control cases if seeding occurred in anearby cloud.

One or more embodiments of the invention include an improved ability ofeach ‘Intelligent’ System to navigate toward candidate cloud area whereseeding is probably effective based on the location coordinatesprovided. The location coordinates can be obtained from theauxiliary/ancillary data inputs and processed, such as by a navigationcontrol-like module, onboard the system. The skilled artisan willappreciate that all regulatory requirements should be complied with, andreasonable and prudent precautions taken; for example, in jurisdictionsand/or under conditions that dictate same, pilot over-ride shouldpreferably be available at all times. The navigation control-like modulecan contain an autonomy routine, or equivalent, which performs a nowcastof the real-time ingested ancillary/auxiliary location coordinates andplatform sensor data (see table of FIG. 5). The output of the autonomyroutine, or equivalent, is then fed back into the navigationcontrol-like module and the system then adapts its path accordingly withongoing in situ sampling as it heads to those new locations. Theautonomy routine, or equivalent, continuously updates seedingcoordinates throughout the flight, but once the ‘Intelligent’ Systemreaches the ideal location, seeding begins and ends once the sensorsindicate favorable and then unfavorable seeding conditions,respectively. The seeding cycle continues, for example, until the UASmust return for fueling or there is an unsafe situation, at which time areplacement system will be in place to continue the activity. The groundcontrol station (GCS) computer can have pre-defined flight plans frommission control software and the position data to generate initialnavigation coordinates for each ‘Intelligent’ System. Conventionalsystems use radar and pilot, or just radar and surface observations toturn on the ground generators. The information used by conventionalsystems not employing embodiments of the invention is not alwaysavailable or if it is available it is very far away and might not berelevant (e.g. of course resolution and/or poor quality).

One or more of embodiments of the invention also include an improved,built-in, ability to conduct randomized seeding (operations orexperiments). That is, using inventive techniques involving, e.g., radarand/or UAV(s), candidate clouds are chosen with minimal human bias, ifany, and then seeded randomly again with minimal, if any, human bias.See row “g” of FIG. 7 for example.

Embodiments include the improved ability of each ground-based(stationary, tethered, and mobile) ‘Intelligent’ System to beautonomously controlled remotely and use its concurrent sensor payloador model simulated data to identify when systems are seedable, selectthe seeding material, decide when to turn on all, one or none of thesystems, and continue the seeding operation until the conditions haveended. The remote control can be performed by model guidance. Hence themodel turns on the system, not a human, in one or more embodiments. Oneor more embodiments provide the capability for human over-ride. Themodel guidance is based on the data from the intelligent ground systems.That data are processed to control the start and stop seeding actions aswell as to control the type of material dispensed, and to keep track ofthe total amount dispensed. Each ‘Intelligent’ system 105, 113 alsoprovides alerts for reloading the seeding materials, and evencommunicates extreme weather conditions. Once seeding ends each systemcontinues to make measurements as required. Further, non-seeding‘Intelligent’ Systems in an array of ground-based ‘Intelligent’ Systemscan be collecting data throughout the same period, concurrently with thesystems that were seeding. Conventional practice is to have a local orremote operator physically turn on or dial up the ground seedinggenerators based on commercially available information that may or maynot be timely, or locally relevant.

Embodiments include an improved ability for each ‘Intelligent’ Systemtransmitting data (i.e., payload, seeding system software,auxiliary/ancillary and autopilot) to the ground control station (GCS)automatically via telemetry for archive and computationally intensiveprocessing. The results from computationally intensive processing can besent back to the ‘Intelligent’ System data management system. Further,each ‘Intelligent’ System is typically able to communicate with eachother throughout a program. A beneficial improvement in one or moreembodiments includes the ease of obtaining needed information to conductand evaluate seeding actions compared to conventional pilot data. Thefirst job of the pilot is to fly his or her plane. The format of his orher information is rarely digitized and typically handwritten when safeto record, which might be well after the final flight for the day. Radardata might be available in some current systems, but with specialsoftware and retrieved after-the-fact with meteorological and streamdata.

One or more embodiments allow inventive technologies to evolveindependently of their use. This translates into more streamlined cloudseeding operations, smaller operational footprint, and lower cost(compared to contemporary cloud seeding programs), while enhancing oreven optimizing the effectiveness of seeding operations. An exemplaryimprovement includes the use of more accurate and safer technologies,compared with the decades old technologies used for conventional cloudseeding. The use of the routinely obtained data from this invention canprovide additional data to enhance the accuracy and development ofdecision support tools applied to current cloud seeding activities.

One or more embodiments provide data at temporal and spatialsensitivities to overcome the predictability or sparseness issues ofenvironmental parameters needed to identify conditions suitable forseeding, and how such might be implemented. These data will also bereadily available for post event evaluation. Current operations oftenrequire after-the-fact retrieval of data for evaluations that are notalways readily available (and even if available might not be relevant intime and space, for example). Furthermore in this regard, consider thatthe data obtained after the fact might not be the exact same parameter;e.g., the needed data might be hydrometeor size, but instead the onlyavailable data obtained might be liquid water content. While liquidwater content is related to hydrometeor size cubed and number density,since the data are obtained after the fact, assumptions would have to bemade to obtain the size. Those assumptions cause uncertainties, and ifused to improve a decision support model could lead to poor qualitydecision support models, that we will (erroneously) be assumed toreflect reality. Conventional cloud seeding methods, whether mannedaircraft or ground-based, rely on data from larger than local/regionalscales to conduct their localized cloud seeding operations. These dataare usually at extra costs as well.

One or more embodiments are not limited to specific sensingtechnologies, seeding delivery systems and their agents, nichecomponents, models or algorithms, and decision support tools foroperations and operational effectiveness, beyond the high levelfunctional needs and the method employed to guide successful use duringweather modification activities as described elsewhere herein. That is,embodiments of the invention are not limited to any particular design orfabrication technique for the ‘Intelligent’ System itself. The presentspecification describes the required capabilities of those ‘Intelligent’Systems including the adaptive autonomy as a function of theirapplication, and the methodology and framework to be employed for theiroptimal use in weather modification or cloud seeding program activities,so that the skilled artisan can readily make and use embodiments of theinvention without undue experimentation.

Embodiments allow “Intelligent’ Systems' that are non-ground-based orairborne, to be flown separately, or in a swarm, or in tandem (i.e., twoor more) to perform a seeding action, and/or in Eulerian and/orLagrangian framework with or without profiling to best meet therequirements of the seeding activity. Embodiments allow “‘Intelligent’Systems” that are ground-based to be used individually, or in anetwork/array configured and controlled to ensure optimal coverage ofthe seeding material in the targeted cloud systems in a manner to bestmeet the requirements of the seeding activity. The seeding can come fromany one, some, or all of the systems. All systems can have their sensorpayload sensors activated throughout the seeding period and beyond.

Embodiments provide a more ‘intelligent,’ safer way to conduct and/orevaluate weather modification or cloud seeding or inadvertent weathermodification operations, which lowers the cloud seeding operationalfootprint and cost, while optimizing operational effectiveness andefficiency compared to current ways to conduct and evaluate suchprograms. Results from the use of sparse environmental information canlead to very costly, wrong decisions or actions, which may result indisastrous events. Embodiments also provide value added benefits to anumber of communities, for example, at no cost to the sponsor: (i) amore accurate understanding into cloud processes and the environment(being seeded while at the cloud performing the seeding), leading to amore accurate seeding operation and more accurate quantification of theimpact by weather modification activities; (ii) a dataset that will helpadvance other science disciplines, improve weather forecasts, contributeto decision support tool development and use; (iii) a truly moreintelligent, robust component of a multidisciplinary solution tominimize the negative impacts from socioeconomic issues related topopulation increase, ecosystem and land cover change, dwindling watersupplies, water security, and their relation with the hydrologicalcycle; and/or (iv) Embodiments facilitate the capability of enhancingthe quality of global lives. For example, embodiments can extend theinfrastructure of more developing countries and countries with limitedinfrastructure, and access to technology that can help provide potablewater to their people.

Some embodiments can make use of machine learning to determine and/orevolve optimal seeding locations and/or materials. Machine learningevolved from the study of pattern recognition and computational learningtheory in artificial intelligence, and explores the study andconstruction of algorithms that can learn from and make predictions ondata. Such algorithms make data-driven predictions or decisions, throughbuilding a model from sample inputs. Supervised learning, unsupervisedlearning, and/or reinforcement learning can be employed, for example.Artificial neural networks, decision trees, and the like are furthernon-limiting examples. As will be appreciated by the skilled artisan,generally, a cognitive neural network includes a plurality of computerprocessors that are configured to work together to implement one or moremachine learning algorithms.

Unmanned aerial vehicles are commonly referred to as drones; they can befixed wing and/or rotary wing and can, in some instances, also includerockets as appropriate as described herein. UAV designs includefuselage/wing assemblies resembling planes as well as helicopter andquadcopter configurations. Sensors such as gyroscopes, accelerometers,altimeters, GPS modules, cameras and/or payload monitors may beincorporated within UAVs. Gimbals may be used to mount payloads in UAVs.Radio signals generated by a transmitter/receiver, a smartphone, atablet or other device can be used to control a UAV. UAVs can bedesigned to operate partially or completely autonomously. Functions suchas hovering and returning to home can, for example, be providedautonomously. Data obtained by UAVs can be stored onboard using, forexample, suitable memory, or transmitted wirelessly. UAVs can beprovided with on-board processing capability and/or can wirelesslytransmit and receive data from a remote controller which has, or iscoupled to, computing capability.

Given the discussion thus far, it will be appreciated that, in generalterms, an exemplary method, according to an aspect of the invention,includes obtaining data including current locations of candidate clouds143 to be seeded. A further step includes, based on the data includingthe current locations of the candidate clouds to be seeded, causing avehicle (e.g., 105-1, 105-2, 113 a, 113 b) to move proximate at leastone of the candidate clouds to be seeded. An even further step includesobtaining, from a sensor suite 255, 275 associated with the vehicle(e.g., via telemetry 289, 291), while the vehicle and sensor suite areproximate the at least one of the candidate clouds to be seeded 143,weather and cloud system data 293, 275, 255.

An additional step includes obtaining (e.g., via telemetry 289, 291)vehicle position parameters from the mission planner(s) 287-1, 287-2using data from the sensor suite 255, 275 associated with the vehicleand data set 293. Another step includes, based on the weather and cloudsystem data 293 and the vehicle position parameters, determining, via amachine learning process, passed via telemetry 283-1, 283-2, 289, 291,251, 271 to system autopilot or remote control 249, 269 and CPU 257,277, which of the candidate clouds should be seeded; and as the systemreaches the candidate clouds for seeding, adding the information from255,275 obtained within the candidate clouds 143 where to disperse anappropriate seeding material. In a non-limiting example, the machinelearning takes place between the ground systems 109 and the vehiclesusing the data 293, and the data from 113 and/or 105.

It is worth noting that, in a conventional system, what material to useis predetermined. Examples include, for seeding, AgI, Dry Ice, or otherse.g., using nanotechnology, titanium oxide-coated materials. However, inone or more embodiments, what material to be used for seeding can alsobe determined from among multiple possibilities via machine learning.One or more embodiments use thresholds for the seeding start/stopdecision; i.e., when to turn the dispensing system on and off. Machinelearning is used in one or more embodiments to determine which cloud toseed and where to put the seeding material. In one exemplary aspect, asophisticated decision tree classifier within the mission planner 287-1,287-2 uses all data, including that from onboard sensors 255, 275 andradar (in dataset 293), to guide the UAV 105/UGV 113 to the cloud system143 and determine when to seed. The gathered data are also available fornext time—that is to say, in one or more embodiments, both real timedata, and stored data 247, 267, 281-1, 281-2 are used. For the next use,historical data plus data from the last use will be available.Ground-based systems will typically be located in a cloud (for example,because on a mountain top) or can be launched up into the cloud(s).

Still another step includes controlling the vehicle to carry out theseeding on the candidate clouds to be seeded, in accordance with thedetermining step; for example, via telemetry 291, 289 to telemetry radio251, 271 communicates and/or with remote control 249, 269, and controlsseeding dispenser 245, 265 or other dispensing system. Telemetry radio(251, 271) also provides weather alerts to GCS. The terminology“dispensing system” or “seeding dispenser” includes a wide variety ofdispensing devices—cloud seeding chemicals may be dispersed by aircraftor by dispersion devices located on the ground (generators orpyrotechnics, in rare cases canisters fired from anti-aircraft guns orfrom within rockets). For release by aircraft, silver iodide flares,solution generators, or the like can be ignited and dispersed as anaircraft flies through the inflow of a cloud. When released by deviceson the ground, the nuclei are carried downwind and upward by aircurrents after release into the candidate clouds.

The aforementioned weather and cloud system data can include, forexample, atmospheric temperature; data indicating humidity; and at leastone of atmospheric aerosol size distribution and atmospheric cloudhydrometeor size distribution. Data can be obtained, for example,inside, or just above the top of, or just below pertinent clouds 143.Appropriate locations from which data can be gathered are generallyreferred to herein as being proximate and or adjacent the cloud(s)during seeding; i.e., in a region where readings are relevant. One ormore embodiments stop seeding when a seeding threshold is no longer met.Such start/stop decisions can be made, for example, based on atmosphericaerosol size distribution and/or atmospheric cloud hydrometeor sizedistribution 275, 255; in one or more embodiments, temperature data isalso used to support the start/stop decision 255, 275, 293. Given theteachings herein, the skilled artisan will know at what temperature tostart and stop seeding. Temperature data 255, 275 can be used in one ormore embodiments to help pick the seeding material—e.g., whether to usedry ice or silver iodide. For example, in one or more embodiments, ifthe atmospheric aerosol size distribution and/or atmospheric cloudhydrometeor size distribution data appears to be favorable, verify thatthe temperature data indicates the correct temperature or that there isan updraft to take the material to a suitable location. This can beobtained, in one or more embodiments, using the sensor suites 255, 275and processing onboard (via CPUs 257/277) ‘Intelligent systems 105, 113and at the ground control stations 109.

In some cases, the aforementioned weather and cloud system data furtherincludes atmospheric pressure; wind components; and cloud imagery. Inone or more embodiments, wind components for the three Cartesiancoordinates (u,v,w or x,y,z or east-west, north-south, or ground totop-of-the-atmosphere) are obtained for both aerial and ground-basedsystems. As used herein, “cloud imagery” includes imaging with visiblelight (e.g. video) as well as imaging with infrared/non-visible light.Cloud imagery is a subset of “cloud system data.” Video can be used, forexample, for the guidance of a human UAS/UGV 105, 113 pilot or to takethe place of a human operator when appropriate. In some instances,imaging is used to verify that the aerial vehicle is proceeding throughthe targeted cloud 143) and remaining within the targeted cloud-imaging,where appropriate, takes the place of a human operator. Image processingvia ground control station computers 109 on the video or other imagingcan be undertaken, and/or the video or a visual representation of theimagery can created for viewing and interpretation by a human. Theresults from such may be telemetered back to the source 105, 113 of thevideo imagery and used to guide those UAS/UGV vehicles 105, 113.

Of course, the rules of pertinent authorities (e.g., the FederalAviation Administration or FAA in the USA or similar authorities inother jurisdictions) should be followed; for example, any rulesrequiring a human observer. In some situations, a ground-based humanobserver may not be able to see a UAV operating inside a cloud system;where appropriate, imaging can be used to observe the cloud and what isgoing on inside it—for example, a human controller observes via video.Alternatively, or in addition, some embodiments undertake real-timevideo processing and have a computer interpret the video images (e.g.using the Romatschke et al. 2017 analysis technique) and emulate thedecisions that would be made by a human controller.

It is worth noting that electrostatic properties from an appropriatesensor 255, 275 can also be used in one or more embodiments to determinewhether there is ice and water or just ice or just water in the cloud.

The aforementioned wind components can include, for example, magnitudeand direction of three vector components.

The determinations made via machine learning can also, in someinstances, include a rate at which to disperse the appropriate seedingmaterial.

In one or more embodiments, the vehicle is an aerial vehicle; the sensorsuite (e.g., 275) is on the aerial vehicle; the step of causing thevehicle to move proximate the at least one of the candidate clouds to beseeded includes via telemetry (e.g., 289) from ground control station109 or operations center (e.g., 299) of output from mission planner(e.g., 287-2), causing the aerial vehicle to fly proximate the at leastone of the candidate clouds 143 to be seeded; and the step of obtainingthe weather and cloud system data includes obtaining the weather andcloud system data from the sensor suite 275 while the aerial vehicle isflying proximate the at least one of the candidate clouds to be seeded.

Note that aerial vehicles can generally be manned or unmanned, fixedwing, rotary wing, or even rockets.

In some cases, the aerial vehicle is an unmanned aircraft vehicle 105;the step of causing the aerial vehicle to fly proximate the at least oneof the candidate clouds 143 to be seeded includes causing a firstcontrol signal to be sent via telemetry 289 from 109 of output frommission planner 287-2 to the unmanned aerial vehicle 105 to cause theunmanned aerial vehicle to fly proximate the at least one of thecandidate clouds 143 to be seeded (e.g. via telemetry command(s)); andthe step of controlling the aerial vehicle includes causing a secondcontrol signal to be sent to the unmanned aerial vehicle to cause theunmanned aerial vehicle to carry out the seeding on the candidate cloudsto be seeded, in accordance with the determining step (e.g. viatelemetry command(s)). For example, the system uses output from sensoralgorithms 263 compared with 287-2, 281-2 information and returned viatelemetry to the UAS (105) via radio 271 then through its CPU 277 to itsseeding dispenser 265. “First” and “second” signals should be understoodto include both (i) separate and distinct signals and/or (ii) differentinformation modulated onto the same carrier.

In some cases, a further step includes obtaining ancillary data from alocation other than the sensor suite on the unmanned aerial vehicle 293;the determining, via the machine learning process, is further based onthe ancillary data 293. Examples of obtaining such ancillary datainclude obtaining from at least one of a manned aircraft; a radarinstallation 101, 111; and another unmanned aerial vehicle (e.g., UAS1105-1 penetrates the cloud to seed while UAS2 105-2 samples the cloudupdraft and the aerosol and cloud hydrometeor properties nearcloudbase—providing at least a portion of the ancillary data). As noted,in some instances, real-time video imagery processing is carried out onvideo feed (or cloud imaging feed using non-visible light) from theunmanned aerial vehicle. This aids, for example, machine learning and/orcontrolling the drone to dispense seed material. In one or moreembodiments, a computer interprets the video images and emulates thedecisions that would be made by a human controller. Image processing viaground control station computers 109 on the video or other imaging canbe undertaken, and/or the video or a visual representation of theimagery can created for viewing and interpretation by a human. Theresults from such may be telemetered back to the source 105, 113 of thevideo imagery and used to guide those UAS/UGV unit(s) 105, 113.

In some cases, the aerial vehicle is a manned aerial vehicle; and thecontrolling of the aerial vehicle to carry out the seeding on thecandidate clouds to be seeded includes communicating (e.g., displaying)results of the determining step from the operations center 299 and inrare cases onboard radar to a human operator of the manned aerialvehicle. The human operator could be a pilot on the aircraft(human-on-board) who is trained to read radar data if available. Inanother aspect, results of machine learning from the ground controlcenter computers 109 are displayed to a human-controlled UAS/droneoperator to facilitate control of the UAS.

In another aspect, in a case where the aerial vehicle is an unmannedaircraft (UAS, 105), further steps include detecting an icing conditionon the UAS (e.g. via sensors 275 and CPU 277, and sensor algorithms263); and, responsive to the detecting, initiating a de-icing procedure.To determine icing condition in an unmanned vehicle, data can beobtained from a temperature, and airspeed sensors 275, andnavigation-related components 269, 287-2. Ancillary data includesinformation needed to fly the craft. Video feed can also be used ifavailable. If the vehicle is in a cloud, and thus near high humidity,temperature and humidity sensors can be used, for example. Someembodiments also monitor pitch, yaw, and air speed. The skilled artisanwill be familiar with psychrometrics and will be able to determine, forexample, that condensation will occur if the wing temperature is at orbelow the dew point. The wet bulb temperature can be used in lieu of thedew point. If the wing temperature is also at or below the freezingpoint of water at the ambient pressure, one can anticipate icing.Suitable de-icing solutions are available, for example, from InnovativeDynamics Inc. of Ithaca, N.Y. One or more embodiments activate a heateror other de-icing scheme when ice is detected.

Of course, manned vehicles can be de-iced in a known manner, as needed.

In instances where the vehicle is a ground vehicle, the step of causingthe vehicle to move proximate the at least one of the candidate cloudsto be seeded includes causing the ground vehicle to drive proximate theat least one of the candidate clouds to be seeded.

In one or more such instances, the sensor suite is on the groundvehicle; and the step of obtaining the weather and cloud system dataincludes obtaining the weather and cloud system data from the sensorsuite while the ground vehicle is driving or stationary and is proximatethe at least one of the candidate clouds to be seeded.

In some cases, the ground vehicle is an unmanned ground vehicle; thestep of causing the ground vehicle to drive proximate the at least oneof the candidate clouds to be seeded includes causing a first controlsignal to be sent to the unmanned ground vehicle to cause the unmannedground vehicle to drive proximate the at least one of the candidateclouds to be seeded; and the step of controlling the ground vehicle tocarry out the seeding on the candidate clouds to be seeded, inaccordance with the determining step, includes causing a second controlsignal to be sent to the unmanned ground vehicle to cause the unmannedground vehicle to carry out the seeding on the candidate clouds to beseeded, in accordance with the determining step. Again, “first” and“second” signals should be understood to include both (i) separate anddistinct signals and/or (ii) different information modulated onto thesame carrier.

In some cases, a further step includes obtaining ancillary data 293 froma location other than the sensor suite 255 on the unmanned groundvehicle 113; the determining, via the machine learning process, isfurther based on the ancillary data. Examples of obtaining suchancillary data include obtaining from at least one of a manned aircraft;an unmanned aircraft 105; a manned ground vehicle; a radar installation101, 111; and another unmanned ground vehicle 113.

In some cases, the ground vehicle is a manned ground vehicle; and thecontrolling of the ground vehicle to carry out the seeding on thecandidate clouds to be seeded includes communicating (e.g. displaying)results of the determining step to a human operator of the manned groundvehicle. That is to say, in one or more embodiments, results of machinelearning from the ground control center computers 109 are displayed to ahuman to facilitate control of the UGV, a driver on ground vehicle(human-on-board) or human-controlled UGV.

In some instances, a further step includes determining, via the machinelearning process, the appropriate seeding material to be used.

In one or more embodiments, the weather and cloud system data 293, 255includes at least one of atmospheric aerosol size distribution andatmospheric cloud hydrometeor size distribution, and further stepsinclude continuing to obtain at least one of atmospheric aerosol sizedistribution and atmospheric cloud hydrometeor size distribution duringthe seeding 255, until a threshold value of the distribution is crossedbased on use of onboard sensor data 255, radar 293 data compared withmission planner-derived values 287-1 as described previously; andcausing the seeding to cease when the threshold is crossed. In one ormore embodiments, an initial pass indicates when and from where to beginseeding; the system then continuously monitors the sensors includingtemperature, windfield 255 and video 253 to determine when and fromwhere (if an array of ground sensors) to stop. Thus, one or moreembodiments continue to monitor while seeding, obtaining the relevantparameters from the platform sensors.

In one or more embodiments, a machine learning module (including, forexample, the components from within the ground control center computers109) is trained from historical 281-1, stored 247 and/or sensor data 255as previously defined herein on an annotated corpus. That is to say, abody (corpus) of data from any appropriate source(s) is annotated by ahuman expert and then used to train the machine learning system. Someportion of the data can be reserved for a test corpus. The step ofdetermining via the machine learning process is then carried out withthe trained machine learning module (preferably verified against thetest corpus) and then passed back to the UGV 113 via telemetry 291, 251.It is worth noting that commercially available navigation systemprograms/flight planning software can determine a path to a point givenits coordinates. One or more embodiments “wrap” such a program and/ormodify the code of same so that it accepts the radar data 293, onboardsensor data 255, 275, and weather data 293 to help determine anappropriate and even optimal path to the cloud to be seeded. Once there(i.e., at cloud 143), the actual data 255, 275 can be used to furtherenhance guidance.

Reference should now be had to FIG. 8; the same illustrates how thesystem integrates/links instrumentation on the payload with the flightplanning software, radar, etc. reference is also again made to DeFeliceand Axisa 2016. FIG. 8 thus provides a non-limiting exemplaryillustration to assist the skilled artisan in implementing a system,including linking the various components such as the flight planningcomponent, radar component which identifies the area where the vehiclestarts looking for the place to do the seeding, and so on.

In one or more embodiments, an Autonomous UAS control routine 269(equally representative of remote control 249) utilizes data from themission planner 287-2 (equally representative of mission planer 287-1)for cloud seeding operations. Sensor data 275 (equally representative ofsensor data 255) and radar data (from dataset 293), acquired in dataacquisition block 801, are processed in a data processing block 803 by,respectively, data quality low pass filter 815 and radar quality controlblock 817. High level control block 805 then applies cloud seedingmodels 261 (equally representative of 241) to provide seeding actions tothe UAS via 269 or UGV via 249. The data quality 815 is performed by thesensor algorithm 263 (equally representative of 243); the radar dataquality control 817 is performed on the ground station CPU 287-2, 287-1.In a non-limiting example, the weather radar software 813 is the TITAN(Thunderstorm Identification, Tracking, Analysis, and Now-casting)software as known from Dixon M. et al., “Titan: thunderstormidentification tracking analysis and nowcasting a radar-basedmethodology,” J Atmos. Ocean Technol. 10: 785-797 (1993) (hereinafter“Dixon et al 1993”), expressly incorporated herein by reference in itsentirety for all purposes, although the skilled artisan will begenerally familiar with same, as further modified to not only now-castthe location of convection, with real-time radar echo data input aboutthe cloud environment, but also with sensor data input from the UAS, asdescribed in DeFelice and Axisa 2016. The Sensor Algorithms and CloudSeeding Model 261, 263 includes, for example, the inputs from the SensorAlgorithms 263 (equally representative of 243), and the cloud seedingmodel 261 as discussed elsewhere herein. In a non-limiting example, thecloud seeding model includes a coalescence model and appropriatemodeling of ice crystal processes and the like, as will be appreciatedby the skilled artisan. Algorithms and model 261, 263 are equallyrepresentative of 241, 243. Autopilot 269 provides low-level control 807based on blocks 263, 813. A UAS pilot 809 and meteorologist 811 have theoption to modify or interrupt the actions taken by the UAS. Elements275, 815, 261, 263, 269 reside on the UAS system; elements 293, 817, 813pertain to the radar system; and elements 809, 811 represent groundoperations (e.g. 299).

As used herein, a vehicle is “proximate” a cloud or cloud system when itis within the cloud or cloud system, or near enough to the cloud orcloud system to obtain useful data. The skilled artisan understands theabilities of sensors and will know how close to a cloud a particulartype of sensor needs to be to obtain useful data.

It is worth noting that atmospheric temperature could be, by way ofexample and not limitation, the dry bulb temperature; and dataindicating humidity can include, by way of example and not limitation,relative humidity, absolute humidity, wet bulb temperature, dew point,mixing ratio, saturation mixing ratio, and the like—whatever parameterspermit calculating humidity (if necessary, in conjunction with the drybulb temperature)—the skilled artisan will be familiar withpsychrometrics and the psychrometric chart.

Regarding wind components, the updraft velocity equals the magnitude ofthe vertical vector component.

It should be noted that atmospheric aerosol size distribution andatmospheric cloud hydrometeor size distribution can range from aerosolson the order of 10⁻⁴ microns up to ˜2×10⁵ microns (the latter caninclude, e.g., what is commonly known as grapefruit-sized or melon-sizedhail).

The skilled artisan will appreciate that when vehicle position andattitude parameters are obtained from “said sensor suite” on thevehicle, it is not necessarily from the same sensors in the sensor suitethat gather the weather and cloud system data—that is to say, differentsensors in the suite provide different data measurements. The skilledartisan will also appreciate that determining “which of said candidateclouds should be seeded” will generally involve ultimately identifyingone or more. Yet further, the skilled artisan will appreciate that the“rate at which to disperse said appropriate seeding material” caninclude, e.g., volumetric or mass flow rate; “where and when to dispersesaid appropriate seeding material” implies for how long, i.e., when tostart and when to stop.

In one or more embodiments, controlling the unmanned aerial vehicle tocarry out the seeding on the candidate clouds to be seeded, inaccordance with the determining step, is carried out in a secure and/orautonomous manner—for example, via adaptive control, a human flying aUAV, or manned flight. It is of course appropriate to comply with alllocal laws, rules, and regulations; to encrypt signals to preventhacking and/or undesired dispensing; and to keep vehicles in a securelocation, locked, and/or otherwise secured. Indeed, appropriate securityprocedures and practices should always be employed depending on theutilization context. UAVs should be physically secured to prevent accessby nefarious persons and/or loading with other than appropriate seedingmaterials. Control signals should be sent in a secure manner.

Operation of one or more embodiments should be in accordance withappropriate rules for the jurisdiction of operation; e.g., FAA rules inthe USA. For example, depending on local rules, it may be appropriate toprovide the option for a human to override the autonomy/adaptivecontrol. Due consideration should also be given to where to place theUAS/UGV. For example, that place may be off the main road, and/or notowned by the organization sponsoring or implementing the program. In thecase of the latter we would get the appropriate permissions.

In some instances, sensor data can be used to help determine when tostart and when to stop dispensing the seeding material. For example, theoutputs from the sensor(s) 275, 255 sensing at least one of atmosphericaerosol size distribution and atmospheric cloud hydrometeor sizedistribution are sent to quality assurance and control within sensoralgorithms 263, 243 following the standard steps under QC step 311, thento ground stations 109-2, 109-1 to compare against outputs from theMachine learning process as defined herein of established thresholdconditions for seeding stored in SIL databases 281-2, 281-1. The resultsof that comparison are returned via telemetry 289, 291 and passed toseeding model 261, 241 that sends a signal via the CPU 277, 257 to theseeding system, seeding dispenser 265, 245 to begin seeding/stop seedingwith the seeding material. When the at least one of atmospheric aerosolsize distribution and atmospheric cloud hydrometeor size distributionchanges to a certain point (i.e., the threshold condition), stopseeding. The skilled artisan, given the teachings herein, can setappropriate thresholds.

Many different items can be determined via machine learning in one ormore embodiments; e.g., what clouds to seed; what seeding material touse; where and when to seed the clouds to be seeded; the path to take toarrive at the location given the in situ meteorological and aviationdata; the mass and/or volume of seeding material to be dispersed perkilometer or other linear unit of flight; and the like. In someinstances, the choice of seeding material can be predetermined; e.g.silver iodide or dry ice. In some cases hygroscopic flares may beappropriate or just silver iodide flares. One or more of temperature,updraft, droplet and other hydrometeor attributes can be used to makethe determination in one or more embodiments; for example, via on-boardinformation (e.g., sensor data 275,255; cloud imaging data 273,253;stored data 267, 247, 281, 293 non-radar data attributes (e.g., if nosensor data available)) and radar data from ancillary dataset 293.

Many different items can be included in the ancillary data 293,supplementing what is on the system, to guide operation or later on toprove that it was successful. Non-limiting examples include cloudimagery such as video other than 273, 253; model outputs for decisionsupport; weather forecasts; numerical weather prediction model outputs;topography; satellite imagery; other ‘Intelligent’ Systems data;climatological; electrical; meteorological; microphysical andmicrochemical observations/measurements/assimilated data; and the like.For example, initially, radar data might indicate “go to location X.”That location X is provided to the UAV's Autopilot 269 via telemetry 289from the ground control station 109-2 radio 283-2 which received theinformation from the computer 285-2 after it processed the informationfrom the mission planner 287-2 and SIL database 281-2. It is desired togo to X because there is a cloud there with certain appropriateenvironmental conditions. There can also be a video or other cloudimagery 273 feed to help to determine where the cloud is once thevehicle gets to that position. The operator at the control center 299can see the feed. The operator may see the cloud and conclude all is inorder; no intervention is necessary. There may also be weather reportinformation 293 and/or other decision support outputs 293; e.g., icingat −10 C level 153; winds 10 mph at −5 C level 151 where the vehicle isflying; and so on. This information is present in the database 281-2after quality assured by a routine on the CPU 285-2, and will becombined with the in situ data from onboard sensors 267 once the vehiclearrives proximate the candidate cloud and makes in situ measurements inand around that cloud 143. Once the vehicle arrives at the cloud to beseeded 143, and is proximate the cloud 143, the database can be updatedto account for additional sensor data obtained proximate the cloud.Further, in case of a sensor failure (e.g. temperature sensor),temperature (or other missing data) can be obtained from another source(e.g., ancillary data set 293, Data 5 “Weather station”). Similarly, incase the particle sensor fails, the mission planner 287-2 instructs theCPU 285-2 to search ancillary data set 293 for a predetermined dataarchive to obtain relevant but degraded quality data to ensure bestpossible data available to make the determination to seed/no seed.Stored past records 267, 247 can also be consulted in some instancesincluding cases of sensor failure for example, as a means to act as a‘fail safe’ mechanism as previously mentioned. This information can beprocessed on the ground (e.g. in computer 285-2 and/or operations center299, making use of SIL database 281-2, for example) and then sent viatelemetry 289 to the UAV. So, data can be used to verify location, aspart of a graceful degradation scheme, as a backup, and so on. Thetopography can similarly be treated together withnavigation/aeronautical parameters, such as pitch, yaw, roll, and thelike. The aircraft should be kept safely within the flight envelope.

Furthermore in this regard, in one or more embodiments, primary storageis in SIL databases 281-1, 281-2 with storage in 247, 267 primarily as afailsafe for the system, just in case connection is lost with GCS, andor with Mission Ops. In one or more embodiments, data storage on thevehicle is minimal except for data acquisition and some real timeprocessing for algorithms. In some cases, where data is deemedunsuitable the vehicle returns to base. Also, in some cases, CPU 285-2searches data set 293 for a predetermined data archive to obtainrelevant but degraded quality data to ensure best possible dataavailable to make the determination to seed/no seed.

Weather forecast output can include icing levels. This can lead toactivation of the UAV's anti-icing system via a control signal from 269after the icing data are sent via telemetry 289 and processing (e.g. atground control station 109) of the sources of ancillary data 293.

As noted, some embodiments employ a training corpus, including dataannotated by human expert to tell what clouds to seed and when based onat least one of atmospheric aerosol size distribution and atmosphericcloud hydrometeor size distribution, and other relevant parameters. Thesystem is then trained on the annotated corpus. Some aspects can bebased on deterministic calculations, such as comparisons to a storedthreshold, in addition to machine learning. Machine learning systemstypically become more robust with time as more data becomes available.One or more embodiments then use in situ data to see whether seedingcriteria are met; if met, seed, else not. Where training and testcorpora are employed, they can be stored, for example, in memoriesassociated with computers 285-1, 285-2, a computer in the operationscenter 299, or even, in some instances, data stores 247, 267 or othermemory available to CPUs 257, 277.

Consider use of radar data. Conventional systems use a software tool toanalyze radar data from existing storms. For example, suppose the radaris turned on, makes a sweep, and five clouds are located; radar datafrom five clouds is now available. The software tool compares this datato its database and determines which cloud to seed. This conventionalapproach uses radar data. In a non-limiting example, this radar data isweather radar data which can be gathered at distances ranging from 1 to250 miles away or, in some instances, 50-250 miles away. In one or moreembodiments, this is taken as a starting point, but data on the platformis used from inside the cloud for comparison with the threshold. Someradar systems look at large (rain) droplets (on the order of 5-6 mm)with a 10 cm band weather radar. Some embodiments use 5 cm band radar todetect drizzle drops (on the order of 1 mm), to get an earlierindication of precipitation.

In one or more embodiments, the RADAR is used to provide data ofpossible candidate clouds. Weather Radar Software 813 (e.g., TiTANsoftware mentioned elsewhere herein) then takes these clouds andcompares these data against previous training data which is previousradar data to help chose the clouds that will most likely respondpositively to seeding as added by the skilled operator. Thus, aspects ofthe invention enhance previous techniques using TiTAN software with insitu data based on more robust data parameters. In this regard, Radardata are crude and are not always of the right part of a cloud for idealseeding. Note the functional relationship between the precipitationhydrometeors and the radar measurement, which is reflectivity (Radarreflectivity is proportional to hydrometer size to the 6^(th) power).So, guessing at the size has the inherent error magnitude of at least 6times the error in the size value estimated, versus using the on boardsensor size distribution data at the more proximate cloud location, asin one or more embodiments, thus employing more robust dataparameter(s), for example.

In some instances, an additional aspect involves deciding what seedingmaterial to use. Some embodiments use new materials such asnanoparticles, which are still at the research and development stage.Conventional materials can also be employed.

Further aspects of exemplary ground-based systems 113 will now bediscussed. In some instances, each ground-based (stationary, tethered,and mobile) unmanned ground vehicle is autonomously controlled remotelyand provided with machine learning capability. Refer to UGV 113 with CPU257 communicating via telemetry 291 with ground control station 109-1having SIL database 281-1, mission planner 287-1, computer 285-1, accessto ancillary data 293, and capability for communication back to CPU 257on UGV 113 via telemetry 291. The ground-based system has an autonomy orremote control (component 249) that is initially controlled remotely bymesoscale and regional NWP model guidance from dataset 293; its on-board(concurrent) sensor payload 255, 253; and/or other UGV 113, UAS 105,and/or other ancillary data 293. Human over-ride capability fromoperations center 299 can be provided in one or more embodiments.

In some instances, guidance is provided via mission planner 287-1 andbased on data set 293 and in situ (on board) UGV data 255 from sensoralgorithms 243, processed via computer 285-1 and/or CPU 257 and used innear-real-time to: (1) optimally control the start and stop seedingactions (seeding model 241 and dispenser 245); (2) determine whether aUGV should become mobile (e.g. under remote control 249) and/or whethera UAS 105 should be used; (3) control the type of material dispensed245, and/or (4) keep track of the total amount dispensed from unit 245.In some cases, the entire sequence just set forth immediately above iscontinuously updated, and the system is capable of machine learning. Insome embodiments, the ground system's concurrent in situ (on board)sensor payload or model-simulated data identify when systems areseedable. In some instances, this aspect is similar to UAS embodiments,except that the UGV might not be moving).

In some instances, when cloud systems are seedable, the autonomy module249 turns on all, one or none of the systems as a function of theenvironmental conditions as measured by sensors 255 with algorithms 243,and continues the seeding operation until the conditions have ended asdetermined by the in situ sensor suite 255 (e.g. on the UGV). In one ormore embodiments, UGV controlling algorithms 249, 287-1 also determinewhether a UGV 113 should become mobile, provide alerts via telemetry 291for reloading the seeding materials, and communicate, via radio 251,extreme weather conditions. Once seeding ends, each system can continueto make measurements as required. Further, non-seeding ‘Intelligent’Systems in an array of UGVs can be employed to collect data throughoutthe same period, concurrently with the systems that were seeding.

In some cases, if it is determined that a given UGV 113 should bemobile, then remote control 249 will move it to the location determinedto yield a suitable and preferably optimal location to seed the cloud,if the cloud is within the range of that UGV. If that UGV is not inrange and the UGV is part of an array, then the mission planner 287-1determines which UGV/UGVs should start seeding at current locationsand/or be moved to start seeding. Some embodiments direct a UAS 105 tothe location if the cloud 143 is not in the range of the UGV 113 andthat UGV 113 is not able to move.

In some cases, data is processed to control the start and stop seedingactions as herein defined as well as to control the type of materialdispensed as herein defined, and to keep track of the total amountdispensed as herein defined. Alerts can also be provided for reloadingthe seeding materials, and even for communicating extreme weatherconditions. It is worth noting that conventional practice is to have alocal or remote operator physically turn on or dial up the groundseeding generators based on commercially available information that mayor may not be timely, or locally relevant.

In some embodiments relating to ground-based systems, obtain dataincluding when candidate clouds can be seeded. Based on data includingthe current location of the unmanned ground vehicle 113, and in situdata 255 to determine seeding signature, cause a control signal to besent to start seeding as herein defined or to move to a ground locationproximate to the candidate cloud 143 to be seeded as herein defined;then, based on in situ data, start seeding as herein defined. Someembodiments obtaining, from a sensor suite on the unmanned groundvehicle 255, while stationary and/or moving proximate at least to thecandidate cloud to be seeded 143, weather and cloud system dataincluding atmospheric temperature; atmospheric pressure; data indicatinghumidity; wind components; and at least one of atmospheric aerosol sizedistribution and atmospheric cloud hydrometeor size distribution.

Some embodiments further include obtaining vehicle position and (whererelevant) attitude parameters from the sensor suite on the unmannedground vehicle. Based on the weather and cloud system data and thevehicle position and (where relevant) attitude parameters, someembodiments determine, via a machine learning process, when within thecandidate clouds, when to disperse the appropriate seeding material; andwhen to stop seeding; and control the unmanned ground vehicle to carryout the seeding on the candidate clouds to be seeded, in accordance withthe determining step.

The wind components can include, for example, magnitude and direction ofthree vector components.

In some instances, determining via machine learning further includes arate at which to disperse the appropriate seeding material.

Some embodiments further include obtaining ancillary data from alocation other than the sensor suite on the unmanned ground vehicle; thedetermining, via the machine learning process, is then further based onthe ancillary data. The ancillary data can be obtained, for example,from at least one of a manned ground vehicle, a radar installation,another unmanned ground vehicle, and an unmanned aircraft system.

It is worth noting that ground-based systems are typically moreterrain-sensitive than aerial systems. Use of video or other cloudimagery is desirable in one or more embodiments. Topography willinfluence seeding in one or more ground-based instantiations.Conventionally, lit flares are deployed on a stationary tall pole, or ablock of dry ice is located in the back of a truck and a driver drivesup and down a pre-determined path. In another aspect, in mountainousterrain, there may be a plurality of stationary ground generators 121,123, 125 that dispense the seeding material into the air, and thenceinto the cloud, under conditions when it will go to the right locationto cause the precipitation fall where it needs to fall (i.e., targetarea 115), depending on the atmospheric and weather conditions. In someinstances, the human operator may not be on-site. For example, the humanoperator may be in Reno, Nev. but may be seeding on the California sideof Lake Tahoe. Thus, the conditions should be forecast remotely (e.g.via operations center 299) in one or more embodiments. Then, a signal issent, or a telephone call is placed to a farmer (non-limiting example ofa landowner where the system is located) or the like to cause thegenerator to be turned on.

Further regarding dependence on terrain/topography, terrain/topographychanges may relate, e.g., to differences in air temperature and/or windfield. Depending on the temperature and wind field profiles, especiallynear the surface, at least aspects may be pertinent: (i) is the airtemperature cold enough to seed; and (ii) does the wind field indicate,based on the vertical component of the wind, whether seeding materialwill travel to a level where it can not only nucleate ice but, giventhat wind profile, travel to the target area? Based on the teachingsherein, a skilled person in the field will be able to deal withterrain/topography issues.

Given the discussion thus far, it will be appreciated that, in generalterms, another exemplary method, according to another aspect of theinvention, includes obtaining, from a ground-based sensor suite (e.g.255) including a plurality of sensors, associated with a ground-basedseeding suite including a plurality of seeding apparatus (e.g. multiplesystems 113 with dispensers 245), weather and cloud system data. Thedata can be obtained, for example, at station 109-1 via telemetry 291. Afurther step includes, based on the weather and cloud system data,determining, via a machine learning process, which individual ones ofthe ground-based seeding apparatus to activate, and when. The machinelearning process can be carried out, for example, in ground controlstation 109-1 using computer 285-1, mission planner 287-1, and SILdatabase 281-1. Still a further step includes sending control signals(e.g. telemetry 291) to the individual ones of the ground-based seedingapparatus (e.g. one or more systems 113), to cause same to emit seedingmaterial (e.g. from dispensers 245), in accordance with the determiningstep. In some instances, fixed seeding apparatus 121, 123, 125 could beemployed. Appropriate components similar to those in UGV 113 could beemployed analogously for the fixed seeding apparatus.

In some instances, none of the ground systems 113 may be appropriatelypositioned for seeding. Thus, in some cases, additional steps includerepeating the obtaining step to obtain different weather and cloudsystem data; based on the different weather and cloud system data,determining, via the machine learning process, that no individual onesof the ground-based seeding apparatus are appropriate to be activated intheir current locations; and sending further control signals (e.g. viatelemetry 291) to at least one of the individual ones of theground-based seeding apparatus 113, to cause same to reposition itselfto an appropriate location for seeding.

In some instances, none of the ground systems 113 may be appropriatelypositioned for seeding, and they may not be mobile or repositioning maynot be practicable. Thus, in some cases, additional steps includerepeating the obtaining step to obtain different weather and cloudsystem data; based on the different weather and cloud system data,determining, via the machine learning process, that no individual onesof the ground-based seeding apparatus are appropriate to be activated intheir current locations; and, responsive to the determining, sendingfurther control signals to cause at least one aerial vehicle (e.g. 105)(in general, manned and/or unmanned) to position itself to anappropriate location for seeding. For example, station 109-1 advisesstation 109-2 via Ethernet 297, or radios 251 and/or 271 communicate, orcommunicate via 129 or a direct communication path between 105-1 and 113a (omitted to avoid clutter).

In some cases, in the obtaining step, the plurality of sensors 255 arecollocated with the plurality of seeding apparatus 245 on a plurality ofground vehicles 113. In other embodiments, separate sensor suites, noton the vehicles, could be used.

In some instances, the step of determining via machine learning furthertakes into account at least one of remote sensing data (e.g. part ofdataset 293) and weather model output data (e.g. part of dataset 293).In this regard, non-limiting examples of remote sensing data includeradar data, LIDAR data, and satellite data (e.g. part of dataset 293).Weather model output data (e.g. part of dataset 293) can also be used(weather model predicts where clouds will go). Both ground-based 113 a,113 b and aerial 105 a, 105 b embodiments can, as appropriate, use bothremote sensing data and weather model data.

Furthermore in this regard, one or more embodiments use weather andradar information (e.g. part of dataset 293) plus information on thevehicle (e.g. from sensors 255, 275) to determine whether to startseeding. For movable vehicles, it is possible to have sensors atlocations without vehicles (e.g. “other data” in dataset 293) anddetermine that a vehicle such as a seeding truck 113 should be movedthere. For example, feed data (e.g. “other data” in dataset 293) intothe machine learning component via telemetry 291 to determine the bestlocation for ground seeding vehicles (e.g. trucks). For example, thedata from the dataset 293 is routed via switch 295, Ethernet 297 (or anyother suitable wired or wireless network), mission planner 287-1, andSIL database 281-1 with processing on computer 285-1. Suppose, purely byway of example and not limitation, that there are three seeding vehicles113 all within a certain distance of each other, and perpendicular tothe wind field. In one or more embodiments, the machine learningcomponent has a subroutine that, given the conditions from the weatherforecast (e.g. part of dataset 293) and the radar (e.g. part of dataset293) and the wind field (e.g. part of dataset 293 plus from sensorpayload 255), determines that if the three vehicles are activated (thevehicle locations are known; the local information from their sensors isavailable), the seeding material will not reach the target area 115.Therefore, it is further determined (for example) to move the firstvehicle north two miles (3.2 km), keep the second vehicle at the samelocation; and move the third vehicle number south two miles (3.2 km).Once the vehicles re-deploy to the new locations based on the machinelearning (say, on the order of one minute), the control system 249instructs them to begin seeding 245. The vehicles re-deploy, forexample, via signals from their remote controls 249 based on camera data253, sensor data 255, and data from dataset 293; and based on machinelearning undertaken by mission planner 287-1 using data 281-1 withprocessing on computer 285-1.

In one or more embodiments, the sensor payload 255 on ground vehicles113 will be similar to the sensor payload 275 on the airborne platforms105. Each ground or aerial vehicle has a seeding system 245, 265attached, in one or more embodiments. Whether or not to seed depends, inone or more embodiments, on data and the results of the machine learningprocess. One or more embodiments, for example, use radar information andweather information, are mindful of topography (typically more importantfor ground-based than airborne systems), and also have a known seedingmaterial available. Using that information, and the video or other cloudimaging to verify presence within a cloud, a determination is madewhether to start seeding or not. In one or more embodiments, groundvehicles can optionally be moved.

It will be further appreciated that one or more embodiments are directedto a system including a memory, and at least one processor, coupled tothe memory, and operative to carry out or otherwise facilitate any one,some, or all of the method steps described herein.

The at least one processor could be, for example, a processor of groundstation computer 285, a processor of an operations center 299, or CPUs257 and/or 277 or one or more of same suitably coupled (e.g., a groundcontrol station processor of computer 285 coupled (e.g., via telemetryor other wireless or even wired techniques in appropriate cases) to aremote vehicle processor 257, 277).

The system can include the additional components depicted in FIGS. 1, 2,and/or 8, for example.

One or more embodiments can make use of software running on a processor(e.g., CPUs 257, 277 or processors of computers 285-1, 285-2 or acomputer in operations center 299). With reference to FIG. 4, such animplementation might employ, for example, a processor 402, a memory 404,and an input/output interface formed, for example, by a display 406 anda keyboard 408. The term “processor” as used herein is intended toinclude any processing device, such as, for example, one that includes aCPU (central processing unit) and/or other forms of processingcircuitry. Further, the term “processor” may refer to more than oneindividual processor. The term “memory” is intended to include memoryassociated with a processor or CPU, such as, for example, RAM (randomaccess memory), ROM (read only memory), a fixed memory device (forexample, hard drive), a removable memory device (for example, diskette),a flash memory and the like. In addition, the phrase “input/outputinterface” as used herein, is intended to include, for example, one ormore mechanisms for inputting data to the processing unit (for example,mouse), and one or more mechanisms for providing results associated withthe processing unit (for example, printer). The processor 402, memory404, and input/output interface such as display 406 and keyboard 408 canbe interconnected, for example, via bus 410 as part of a data processingunit 412. Suitable interconnections, for example via bus 410, can alsobe provided to a network interface 414, such as a network card, whichcan be provided to interface with a computer network, and to a mediainterface 416, such as a diskette or CD-ROM drive, which can be providedto interface with media 418 (a USB port interfacing with a so-called“thumb” drive is another example).

The network interface can also be envisioned as representing a wirelessand/or wired data link. Not every instance will have a keyboard anddisplay. For example, a computing unit on a UAV may have a processor,memory, and wireless transceiver, while that in a ground control unitmay have a keyboard and display as well as a processor, memory, andwireless transceiver. Where analog sensors are used, suitableanalog-to-digital (A/D) converters can be employed.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in one or more of the associated memory devices (for example,ROM, fixed or removable memory) and, when ready to be utilized, loadedin part or in whole (for example, into RAM) and implemented by a CPU.Such software could include, but is not limited to, firmware, residentsoftware, microcode, and the like.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 412 as shown in FIG. 4)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

These computer program instructions may also be stored in a computerreadable medium that can configure a processor to function in aparticular manner, such that the instructions stored in the computerreadable medium cause the processor to carry out learning, adaptation,and control functionality.

Thus, FIG. 4 is representative of aspects of a processor and memory thatmay be on a UAV and/or at a ground station or operations center, and thememory can include memory associated with a processor as well as acomputer-readable medium or other non-volatile memory from whichinstructions can be loaded.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the elements depicted in the blockdiagrams and/or described herein; by way of example and not limitation,one exemplary module is a machine learning module as described herein.The method steps can then be carried out using the distinct softwaremodules and/or sub-modules of the system, as described above, executingon one or more hardware processors 402. Further, a computer programproduct can include a computer-readable storage medium with code adaptedto be implemented to carry out one or more method steps describedherein, including the provision of the system with the distinct softwaremodules.

In any case, it should be understood that the components illustratedherein may be implemented in various forms of hardware, software, orcombinations of hardware and software. Application specific integratedcircuit(s) (ASICS), field-programmable gate arrays (FPGAs), functionalcircuitry, one or more appropriately programmed general purpose digitalcomputers with associated memory, and the like, are all examples.

Given the teachings of the invention provided herein, one of ordinaryskill in the related art will be able to contemplate otherimplementations.

The following list of acronyms and abbreviations is provided for theconvenience of the reader:

-   -   AgI—Silver Iodide    -   AI—Artificial intelligence    -   AiMS—Atmospheric icing conditions measurement system    -   ANSI—American National Standards Institute    -   ASCE—American Society Civil Engineers    -   ASICS—Application specific integrated circuit(s)    -   BCPD—Back-scatter cloud probe with polarization detection    -   CAS—Cloud and Aerosol Spectrometer    -   CDP—Cloud droplet probe    -   CMMI—Capability Maturity Model Integration    -   CPI—Cloud particle imager    -   CPU—Central processing unit    -   CWIP—Cloud water inertial probe    -   C4ISR—Communications, Computers, Intelligence, Surveillance, and        Reconnaissance    -   DI—Dry ice    -   DMT—Droplet Measurement Technologies Inc.    -   DSD—Droplet size distribution    -   EWRI—Environmental Water Resources Institute    -   FAA—Federal Aviation Administration    -   FPGA—Field programmable gate arrays    -   GCS—Ground control station    -   GPS—Global positioning system    -   LIDAR—Light Detection and Ranging    -   MI—Machine intelligence    -   NI—Natural intelligence    -   NWP—Numerical weather prediction    -   PMO—Program management office or organization    -   POPS—Printed optical particle spectrometer    -   QA—Quality assurance    -   QF1—Quality Flag per first pass.    -   RADAR—Radio Detection and Ranging    -   RAM—Random access memory    -   ROM—Red only memory    -   SIL—Software-in-the-loop    -   SLW—Supercooled liquid water    -   UAV—Unmanned airborne vehicle; sometimes called unmanned aerial        vehicle.    -   UAS—Unmanned aircraft system    -   UGV—Unmanned Ground Vehicle    -   US—United States (of America)    -   USB—Universal serial bus    -   WMA—Weather Modification Association    -   ZDR—Differential reflectivity    -   3D—Three dimensional

What is claimed is:
 1. A method comprising: obtaining data comprisingcurrent locations of candidate clouds to be seeded; based on said datacomprising said current locations of said candidate clouds to be seeded,causing a vehicle to move proximate at least one of said candidateclouds to be seeded; obtaining, from a sensor suite associated with saidvehicle, while said vehicle and sensor suite are proximate said at leastone of said candidate clouds to be seeded, weather and cloud systemdata, said weather and cloud system data comprising at least one ofatmospheric aerosol size distribution and atmospheric cloud hydrometeorsize distribution; obtaining vehicle position parameters from saidsensor suite associated with said vehicle; based on said weather andcloud system data and said vehicle position parameters, determining, viaa machine learning process: an appropriate seeding material to be used;which of said candidate clouds should be seeded; and within those ofsaid candidate clouds which should be seeded, where to disperse saidappropriate seeding material; and a rate at which to disperse saidappropriate seeding material, wherein determining said rate comprisesestimating a rate of seeding required to modify measured droplet sizedistribution for a seeding effect; and wherein said appropriate seedingmaterial to be used and the rate at which to disperse said appropriateseeding material are based at least on temperature and updraft velocity;and controlling said vehicle to carry out said seeding on said candidateclouds to be seeded, in accordance with said determining step.
 2. Themethod of claim 1, wherein, in said step of obtaining said weather andcloud system data, said weather and cloud system data further comprises:atmospheric temperature; and data indicating humidity.
 3. The method ofclaim 2, wherein, in said step of obtaining said weather and cloudsystem data, said weather and cloud system data further comprises:atmospheric pressure; wind components; and cloud imagery.
 4. The methodof claim 3, wherein, in said step of obtaining said weather and cloudsystem data, said wind components comprise magnitude and direction ofthree vector components.
 5. The method of claim 1, wherein: said vehiclecomprises an aerial vehicle; said sensor suite is on said aerialvehicle; said step of causing said vehicle to move proximate said atleast one of said candidate clouds to be seeded comprises causing saidaerial vehicle to fly proximate said at least one of said candidateclouds to be seeded; and said step of obtaining said weather and cloudsystem data comprises obtaining said weather and cloud system data fromsaid sensor suite while said aerial vehicle is flying proximate said atleast one of said candidate clouds to be seeded.
 6. The method of claim5, wherein: said aerial vehicle comprises an unmanned aerial vehicle;said step of causing said aerial vehicle to fly proximate said at leastone of said candidate clouds to be seeded comprises causing a firstcontrol signal to be sent to said unmanned aerial vehicle to cause saidunmanned aerial vehicle to fly proximate said at least one of saidcandidate clouds to be seeded; and said step of controlling said aerialvehicle to carry out said seeding on said candidate clouds to be seeded,in accordance with said determining step, comprises causing a secondcontrol signal to be sent to said unmanned aerial vehicle to cause saidunmanned aerial vehicle to carry out said seeding on said candidateclouds to be seeded, in accordance with said determining step.
 7. Themethod of claim 6, further comprising obtaining ancillary data from alocation other than said sensor suite on said unmanned aerial vehicle,wherein said determining, via said machine learning process, is furtherbased on said ancillary data.
 8. The method of claim 7, wherein saidancillary data is obtained from at least one of a manned aircraft, aradar installation, and another unmanned aerial vehicle.
 9. The methodof claim 6, further comprising carrying out real-time video processingon cloud imagery feed from said unmanned aerial vehicle.
 10. The methodof claim 5, wherein: said aerial vehicle comprises a manned aerialvehicle; and said controlling of said aerial vehicle to carry out saidseeding on said candidate clouds to be seeded comprises communicatingresults of said determining step to a human operator of said mannedaerial vehicle.
 11. The method of claim 5, wherein said aerial vehiclecomprises an unmanned aerial vehicle, further comprising: detecting anicing condition on said aerial vehicle; and responsive to saiddetecting, initiating a de-icing procedure.
 12. The method of claim 1,wherein: said vehicle comprises a ground vehicle; said step of causingsaid vehicle to move proximate said at least one of said candidateclouds to be seeded comprises causing said ground vehicle to driveproximate said at least one of said candidate clouds to be seeded. 13.The method of claim 12, wherein: said sensor suite is on said groundvehicle; and said step of obtaining said weather and cloud system datacomprises obtaining said weather and cloud system data from said sensorsuite while said ground vehicle is driving or stationary and isproximate said at least one of said candidate clouds to be seeded. 14.The method of claim 13, wherein: said ground vehicle comprises anunmanned ground vehicle; said step of causing said ground vehicle todrive proximate said at least one of said candidate clouds to be seededcomprises causing a first control signal to be sent to said unmannedground vehicle to cause said unmanned ground vehicle to drive proximatesaid at least one of said candidate clouds to be seeded; and said stepof controlling said ground vehicle to carry out said seeding on saidcandidate clouds to be seeded, in accordance with said determining step,comprises causing a second control signal to be sent to said unmannedground vehicle to cause said unmanned ground vehicle to carry out saidseeding on said candidate clouds to be seeded, in accordance with saiddetermining step.
 15. The method of claim 14, further comprisingobtaining ancillary data from a location other than said sensor suite onsaid unmanned ground vehicle, wherein said determining, via said machinelearning process, is further based on said ancillary data.
 16. Themethod of claim 15, wherein said ancillary data is obtained from atleast one of a manned aircraft, an unmanned aircraft, a manned groundvehicle, a radar installation, and another unmanned ground vehicle. 17.The method of claim 13, wherein: said ground vehicle comprises a mannedground vehicle; and said controlling of said ground vehicle to carry outsaid seeding on said candidate clouds to be seeded comprisescommunicating results of said determining step to a human operator ofsaid manned ground vehicle.
 18. The method of claim 1, comprising:continuing to obtain at least said at least one of atmospheric aerosolsize distribution and atmospheric cloud hydrometeor size distributionduring said seeding, until a threshold value of said distribution iscrossed, said threshold comprising a threshold for yieldingprecipitation determined using a decision tree classifier; and causingsaid seeding to cease when said threshold is crossed.
 19. The method ofclaim 1, wherein said step of determining via machine learning furthertakes into account at least one of remote sensing data and weather modeloutput data.
 20. The method of claim 1, further comprising training amachine learning module on an annotated corpus, wherein said step ofdetermining via said machine learning process is carried out with saidtrained machine learning module.
 21. A method comprising: obtaining,from a ground-based sensor suite comprising a plurality of sensors,associated with a ground-based seeding suite comprising a plurality ofseeding apparatus, weather and cloud system data, said weather and cloudsystem data comprising at least one of atmospheric aerosol sizedistribution and atmospheric cloud hydrometeor size distribution; basedon said weather and cloud system data, determining, via a machinelearning process, an appropriate seeding material to be used; whichindividual ones of said ground-based seeding apparatus to activate, andwhen; and a rate at which to disperse said appropriate seeding material,wherein determining said rate comprises estimating a rate of seedingrequired to modify measured droplet size distribution for a seedingeffect; and wherein said appropriate seeding material to be used and therate at which to disperse said appropriate seeding material are based atleast on temperature and updraft velocity; and sending control signalsto said individual ones of said ground-based seeding apparatus, to causesame to emit seeding material, in accordance with said determining step.22. The method of claim 21, further comprising: repeating said obtainingstep to obtain different weather and cloud system data, said differentweather and cloud system data comprising at least one of differentatmospheric aerosol size distribution and different atmospheric cloudhydrometeor size distribution; based on said different weather and cloudsystem data, determining, via said machine learning process, that noindividual ones of said ground-based seeding apparatus are appropriateto be activated in their current locations; and sending further controlsignals to at least one of said individual ones of said ground-basedseeding apparatus, to cause same to reposition itself to an appropriatelocation for seeding.
 23. The method of claim 21, further comprising:repeating said obtaining step to obtain different weather and cloudsystem data, said different weather and cloud system data comprising atleast one of different atmospheric aerosol size distribution anddifferent atmospheric cloud hydrometeor size distribution; based on saiddifferent weather and cloud system data, determining, via said machinelearning process, that no individual ones of said ground-based seedingapparatus are appropriate to be activated in their current locations;and responsive to said determining, sending further control signals tocause at least one aerial vehicle to position itself to an appropriatelocation for seeding.
 24. The method of claim 21, wherein, in saidobtaining step, said plurality of sensors are collocated with saidplurality of seeding apparatus on a plurality of ground vehicles. 25.The method of claim 21, wherein said step of determining via machinelearning further takes into account at least one of remote sensing dataand weather model output data.
 26. A system comprising: a memory; and atleast one processor, coupled to said memory, and operative to: obtaindata comprising current locations of candidate clouds to be seeded;based on said data comprising said current locations of said candidateclouds to be seeded, cause a vehicle to move proximate at least one ofsaid candidate clouds to be seeded; obtain, from a sensor suiteassociated with said vehicle, while said vehicle and sensor suite areproximate said at least one of said candidate clouds to be seeded,weather and cloud system data, said weather and cloud system datacomprising at least one of atmospheric aerosol size distribution andatmospheric cloud hydrometeor size distribution; obtain vehicle positionparameters from said sensor suite associated with said vehicle; based onsaid weather and cloud system data and said vehicle position parameters,determine, via a machine learning process: an appropriate seedingmaterial to be used; a rate at which to disperse said appropriateseeding material, wherein determining said rate comprises estimating arate of seeding required to modify measured droplet size distributionfor a seeding effect; and wherein said appropriate seeding material tobe used and the rate at which to disperse said appropriate seedingmaterial are based at least on temperature and updraft velocity; andwhich of said candidate clouds should be seeded; and within those ofsaid candidate clouds which should be seeded, where to disperse saidappropriate seeding material; and control said vehicle to carry out saidseeding on said candidate clouds to be seeded, in accordance with saiddetermining step.
 27. The system of claim 26, wherein said at least oneprocessor comprises a ground control station processor coupled to aremote vehicle processor.
 28. A system comprising: a memory; and atleast one processor, coupled to said memory, and operative to: obtain,from a ground-based sensor suite comprising a plurality of sensors,associated with a ground-based seeding suite comprising a plurality ofseeding apparatus, weather and cloud system data, said weather and cloudsystem data comprising at least one of atmospheric aerosol sizedistribution and atmospheric cloud hydrometeor size distribution; basedon said weather and cloud system data, determine, via a machine learningprocess, an appropriate seeding material to be used; and whichindividual ones of said ground-based seeding apparatus to activate, andwhen; and a rate at which to disperse said appropriate seeding material,wherein determining said rate comprises estimating a rate of seedingrequired to modify measured droplet size distribution for a seedingeffect; and wherein said appropriate seeding material to be used and therate at which to disperse said appropriate seeding material are based atleast on temperature and updraft velocity; and send control signals tosaid individual ones of said ground-based seeding apparatus, to causesame to emit seeding material, in accordance with said determining step.29. The method of claim 7, wherein said unmanned aerial vehiclecomprises a first unmanned aerial vehicle, and said ancillary data isobtained from a second unmanned aerial vehicle, further comprising:positioning said first unmanned aerial vehicle within a given one ofsaid candidate clouds to be seeded; and positioning said second unmannedaerial vehicle downwind of said first unmanned vehicle where said secondunmanned aerial vehicle samples at least an updraft of said given one ofsaid candidate clouds to be seeded to obtain at least a portion of saidancillary data.
 30. The method of claim 29, further comprising, prior tocarrying out said steps, testing an initial candidate cloud to be seededwith said second unmanned aerial vehicle and determining that saidinitial candidate cloud to be seeded is not suitable for seeding,wherein causing said first unmanned aerial vehicle to move proximatesaid at least one of said candidate clouds to be seeded is responsive tosaid determining that said initial candidate cloud to be seeded is notsuitable for seeding.
 31. The method of claim 30, wherein said machinelearning process comprises a mission planner interrogating asoftware-in-the-loop database, said mission planner further determininga flight path for each of said first and second unmanned vehicles,further comprising repeating said steps for a plurality of missions andupdating said software-in-the-loop database after each of said missions.32. The method of claim 15, wherein said unmanned ground vehiclecomprises a first unmanned ground vehicle, and said ancillary data isobtained from a second unmanned ground vehicle, further comprising:positioning said first unmanned ground vehicle within a given one ofsaid candidate clouds to be seeded; and positioning said second unmannedground vehicle downwind of said first unmanned ground vehicle where saidsecond unmanned ground vehicle samples at least an updraft of said givenone of said candidate clouds to be seeded to obtain at least a portionof said ancillary data.
 33. The method of claim 32, further comprising,prior to carrying out said steps, testing an initial candidate cloud tobe seeded with said second unmanned ground vehicle and determining thatsaid initial candidate cloud to be seeded is not suitable for seeding,wherein causing said first unmanned ground vehicle to move proximatesaid at least one of said candidate clouds to be seeded is responsive tosaid determining that said initial candidate cloud to be seeded is notsuitable for seeding.
 34. The method of claim 33, wherein said machinelearning process comprises a mission planner interrogating asoftware-in-the-loop database, said mission planner further determininga path for each of said first and second unmanned ground vehicles,further comprising repeating said steps for a plurality of missions andupdating said software-in-the-loop database after each of said missions.